Deep Learning for Medical ImageBased Cancer Diagnosis
Abstract
:Simple Summary
Abstract
1. Introduction
 (i)
 The principle and application of radiological and histopathological images in cancer diagnosis are introduced in detail;
 (ii)
 This paper introduces 9 basic architectures of deep learning, 12 classical pretrained models, and 5 typical methods to overcome overfitting. In addition, advanced deep neural networks are introduced, such as vision transformers, transfer learning, ensemble learning, graph neural network, and explainable deep neural networks;
 (iii)
 The application of deep learning technology in medical imaging cancer diagnosis is deeply analyzed, including image classification, image reconstruction, image detection, image segmentation, image registration, and image fusion;
 (iv)
 The current challenges and future research hotspots are discussed and analyzed around data, labels, and models.
2. Common Imaging Techniques
2.1. Computed Tomography
2.2. Magnetic Resonance Imaging
MRI  Description 

Conception 

Feature 

2.3. Ultrasound
Ultrasound  Description 

Conception 

Feature 

2.4. Xray
Xray  Description 

Conception 

Feature 

2.5. Positron Emission Tomography
PET  Description 

Conception 

Feature 

2.6. Histopathology
3. Deep Learning
3.1. Basic Model
3.1.1. Convolutional Neural Network
3.1.2. Fully Convolutional Network
3.1.3. Autoencoder
Author  Method  Year  Description  Feature 

Bengio et al. [159]  SAE ^{1}  2007  Use layerwise training to learn network parameters.  The pretrained network fits the structure of the training data to a certain extent, which makes the initial value of the entire network in a suitable state, which is convenient for the supervised stage to speed up the iterative convergence. 
Vincent et al. [160]  DAE ^{2}  2008  Add random noise perturbation to the input data.  Representation reconstructs highlevel information from chaotic information, allowing high learning capacity while preventing learning a useless identity function in the encoder and decoder, improving algorithm robustness, and obtaining a more efficient representation of the input. 
Vincent et al. [152]  SDAE ^{3}  2010  Multiple DAEs are stacked together to form a deep architecture. The input is corroded (noised) only during training; once the training is complete, there is no need to corrode.  It has strong feature extraction ability and good robustness. It is just a feature extractor and does not have a classification function. 
Ng [151]  Sparse autoencoder  2011  A regular term controlling sparsity is added to the original loss function.  Features can be automatically learned from unlabeled data and better feature descriptions can be given than the original data. 
Rifai et al. [154]  CAE ^{4}  2011  The autoencoder object function is constrained by the encoder’s Jacobian matrix norm so that the encoder can learn abstract features with antijamming.  It mainly mines the inherent characteristics of the training samples, which entails using the gradient information of the samples themselves. 
Masci et al. [161]  Convolutional autoencoder  2011  Utilizes the unsupervised learning method of the traditional autoencoder, combining the convolution and pooling operations of the convolutional neural network.  Through the convolution operation, the convolutional autoencoder can well preserve the spatial information of the twodimensional signal. 
Kingma et al. [153]  VAE ^{5}  2013  Addresses the problem of nonregularized latent spaces in autoencoders and provides generative capabilities for the entire space.  It is probabilistic and the output is contingent; new instances that look like input data can be generated. 
Srivastava et al. [162]  Dropout autoencoder  2014  Reduce the expressive power of the network and prevent overfitting by randomly disconnecting the network.  The degree of overfitting can be reduced and the training time is long. 
Srivastava et al. [163]  LAE ^{6}  2015  Compressive representations of sequence data can be learned.  Representation helps improve classification accuracy, especially when there are few training examples. 
Makhzani et al. [164]  AAE ^{7}  2015  An additional discriminator network is used to determine whether hidden variables of dimensionality reduction are sampled from prior distributions.  Minimize the reconstruction error of traditional autoencoders; match the aggregated posterior distribution of the latent variables of the autoencoder with an arbitrary prior distribution. 
Xu et al. [155]  SSAE ^{8}  2015  Advanced feature representations of pixel intensity can be captured in an unsupervised manner.  Only advanced features are learned from pixel intensity to identify the distinguishing features of the kernel; efficient coding can be achieved. 
Higgins et al. [165]  betaVAE  2017  betaVAE is a generalization of VAE that only changes the ratio between reconstruction loss and divergence loss. The scalar β denotes the influence factor of the divergence loss.  The potential channel capacity and independence constraints can be balanced with the reconstruction accuracy. Training is stable, makes few assumptions about the data, and relies on tuning a single hyperparameter. 
Zhao et al. [166]  infoVAE  2017  The ELBO objective is modified to address issues where variational autoencoders cannot perform amortized inference or learn meaningful latent features.  Significantly improves the quality of variational posteriors and allows the efficient use of latent features. 
Van Den Oord et al. [167]  vqVAE ^{9}  2017  Combining VAEs with vector quantization for discrete latent representations.  Encoder networks output discrete rather than continuous codes; priors are learned rather than static. 
Dupont [168]  JointVAE  2018  Augment the continuous latent distribution of a variational autoencoder using a relaxed discrete distribution and control the amount of information encoded in each latent unit.  Stable training and large sample diversity, modeling complex continuous and discrete generative factors. 
Kim et al. [169]  factorVAE  2018  The algorithm motivates the distribution of the representation so that it becomes factorized and independent in the whole dimension.  It outperforms βVAE in disentanglement and reconstruction. 
3.1.4. Deep Convolutional Extreme Learning Machine
3.1.5. Recurrent Neural Network
3.1.6. Long ShortTerm Memory
3.1.7. Generative Adversarial Network
3.1.8. Deep Belief Network
3.1.9. Deep Boltzmann Machine
3.2. Classical Pretrained Model
3.2.1. LeNet5
3.2.2. AlexNet
3.2.3. ZFNet
3.2.4. VGGNet
3.2.5. GoogLeNet
3.2.6. ResNet
3.2.7. DenseNet
3.2.8. MobileNet
3.2.9. ShuffleNet
3.2.10. SqueezeNet
3.2.11. XceptionNet
3.2.12. Unet
3.3. Advanced Deep Neural Network
3.3.1. Transfer Learning
 (i)
 Instancebased transfer learning entails reusing part of the data in the source domain through the heavy weight method of the target domain learning;
 (ii)
 Featurerepresentation transfer learning aims to learn a good feature representation through the source domain, encode knowledge in the form of features, and transfer it from source domain to the target domain for improving the effect of target domain tasks. Featurebased transfer learning is based on the assumption that the target and source domains share some overlapping common features. In featurebased methods, a feature transformation strategy is usually adopted to transform each original feature into a new feature representation for knowledge transfer [279];
 (iii)
 Parametertransfer learning means that the tasks of the target domain and source domain share the same model parameters or obey the same prior distribution. It is based on the assumption that individual models for related tasks should share a prior distribution of some parameters or hyperparameters. Generally, there are usually two specific ways to achieve this. One is to initialize a new model with the parameters of the source model and then finetune it. Secondly, the source model or some layers in the source model are solidified as feature extractors in the new model. Then an output layer is added for the target problem and learning on this basis can effectively utilize previous knowledge and reduce training costs [274];
 (iv)
 Relationalknowledge transfer learning involves knowledge transfer between related domains, which needs to assume that the source and target domains are similar and can share some logical relationship, and attempts to transfer the logical relationship among data from the source domain to the target domain.
3.3.2. Ensemble Learning
3.3.3. Graph Neural Network
3.3.4. Explainable Deep Neural Network
3.3.5. Vision Transformer
 (i)
 The image of H × W × C is changed into a sequence of N × (P^{2} × C), where P is the size of the image block. This sequence can be viewed as a series of flattened image patches. That is, the image is cut into small patches and then flattened. The sequence contains a total of N = H × W/P^{2} image patches and the dimension of each image patch is (P^{2} × C). After the above transformation, N can be regarded as the length of the sequence;
 (ii)
 Since the dimension of each image patch is (P^{2} × C) and the vector dimension we actually need is D, we also need to Embed the image patch. That is, each image patch will be linearly transformed and the dimension will be compressed to D.
3.4. Overfitting Prevention Technique
3.4.1. Batch Normalization
3.4.2. Dropout
3.4.3. Weight Initialization
3.4.4. Data Augmentation
4. Application of Deep Learning in Cancer Diagnoses
4.1. Image Classification
4.2. Image Detection
4.3. Image Segmentation
4.4. Image Registration
4.5. Image Reconstruction
4.6. Image Synthesis
5. Discussion
5.1. Data
5.1.1. Less Training Data
5.1.2. Class Imbalance
5.1.3. Image Fusion
5.2. Label
5.2.1. Insufficient Annotation Data
5.2.2. Noisy Labels
5.2.3. Supervised Paradigm
5.3. Model
5.3.1. Model Explainability
5.3.2. Model Robustness and Generalization
5.4. Radiomics
6. Conclusions
6.1. Limitations and Challenges
 (i)
 Datasets problems.
 (ii)
 The model lacks explainability.
 (iii)
 Poor generalization ability.
 (iv)
 Lack of highperformance models for multimodal images.
6.2. Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name  Medium  Imaging Method  Imaging Basis  Features  Advantages  Disadvantages  Radiation  Use Cases 

MRI  Magnetic fields and radio waves  Mathematical reconstruction  A variety of parameters  Tomographic images, multiparameter, multisequence imaging, rich grayscale information.  High softtissue resolution.  Long examination time, prone to motion artifacts, low spatial resolution, not suitable for patients with metal parts, high price.  No  soft tissue [23], nervous tissue [24], internal organs [25], etc. 
Xray  Ionizing radiation  Transmission projection  Density and thickness  Strong penetrability, wide dynamic range, suitable for image diagnosis with small grayscale differences.  Full picture, realtime, fine image, low cost.  Limited density resolution, overlapping images, poor identification of soft tissues.  Yes  Skeletal system [26], gastrointestinal tract [27], cardiovascular angiography and dynamic observation [28,29], etc. 
CT  Ionizing radiation  Mathematical reconstruction  Absorption coefficient  Tomographic image, grayscale image, higher grayscale, can display the tissue density of the human body section.  Fast imaging speed, highdensity resolution, no image overlap, further quantitative analysis.  Low spatial resolution, artifacts, and partial volume effects, reflecting only anatomical features.  Yes  Bones [30], lungs, internal organs [31], angiography [32,33], etc. 
US  Sound waves  Mathematical reconstruction  Acoustic impedance interface  Suitable for moderate acoustic tissue measurements (soft tissue, muscle, etc.) and imaging of human anatomy and blood flow.  Safe and reliable, no radiation, low cost, can detect very subtle diseased tissue, realtime dynamic imaging.  Poor image contrast, limited field of view, difficult in displaying normal tissue and large lesions.  No  Abdominal organs [34], heart [35,36], ophthalmology [37], obstetrics and gynecology [38], etc. 
PET  Radioactive tracer  Mathematical reconstruction  Using positron radionuclide labeling  Concentration image of positrons, showing biological metabolic activity.  High sensitivity, high specificity, wholebody imaging, accurate location can achieve the purpose of early diagnosis.  Low image clarity, poor specificity for inflammation, expensive, the examiner needs to have rich experience.  Yes  Brain blood flow [39], metabolic activity [40], cancer diagnosis [41], etc. 
CT  Description 

Conception 

Feature 

Histopathological Image  Description 

Conception 

Feature 

Name  Brief Description  Basic Module 

CNN [129]  Feedforward neural network with convolutional computation and deep structure.  It consists of an input layer, an output layer, and multiple hidden layers. The hidden layers can be divided into convolutional layers, pooling layers, RELU, and fully connected layers. 
FCN [130]  Pixellevel classification of images solves the problem of semanticlevel image segmentation.  All layers in the model network are convolutional layers. 
AE [131]  Efficient feature extraction and feature representation for highdimensional data using unsupervised learning.  Encoder and decoder. 
DCELM [132]  Combining the feature abstraction performance of convolutional neural networks and the fast training of extreme learning machines  It consists of an input layer, an output layer, and multiple alternating convolutional and pooling layers. 
RNN [133]  A neural network with shortterm memory is often used to process timeseries data.  It consists of an input layer, a recurrently connected hidden layer, and an output layer. 
LTSM [134]  A special kind of RNN capable of learning long dependencies.  A cell, an input gate, an output gate, and a forget gate. 
GAN [135]  A deep generative model based on adversarial learning.  Generator and discriminator. 
DBN [136]  The number of layers is increased by stacking multiple RBMs to increase expressive power.  Multilayer RBM. 
DBM [137]  A stack of multilayer RBMs. The middle layer is bidirectionally connected to the adjacent layer.  Boltzmann distribution. 
Name  Year  Brief Description 

LeNet5 [129]  1998  It was designed to solve the problem of handwritten digit recognition and is considered one of the pioneering works of CNN. 
AlexNet [208]  2012  The first deep convolutional neural network structure on largescale image datasets. 
ZFNet [209]  2014  Visualize and understand convolutional networks. 
VGGNet [210]  2014  Deeper architecture and simpler form. 
GoogLeNet [211]  2014  The introduced Inception combines feature information of different scales to obtain better feature representation. 
Unet [212]  2015  Convolutional networks for biomedical image segmentation. 
ResNet [213]  2016  The internal residual block uses a skip connection which alleviates the gradient disappearance problem caused by increasing the depth in the deep neural network. 
MobileNet [214]  2016  Lightweight CNN for mobile vision problems. 
SqueezeNet [215]  2016  Use the fire module to compress parameters. 
DarkNet [216]  2016  An open source neural network framework written in C and CUDA. 
DenseNet [217]  2017  Reusage of feature maps. 
XceptionNet [218]  2017  Better performed than Inceptionv3. 
InceptionResNet [219]  2017  Residual connections were used to increase the training speed of Inception networks. 
ShuffleNet [220]  2018  Lightweight CNN using pointwise group convolution and channel shuffle. 
NasNet [221]  2018  An architectural building block is searched on a small dataset and then transferred to a larger dataset. 
EfficientNet [222]  2019  A compound scaling method with extremely high parametric efficiency and speed. 
Brief Description  Description  Typical Representative 

Dense connection mechanism  There is a direct connection between any two layers.  Dense UNet [253]; Denseblock UNet [254] 
Residual connection mechanism  The convolution layer of UNet is replaced with a residual block. The skip connection uses a residual connection path. The encoder and decoder are replaced by a residual network.  Residual UNet [255]; RRAUNet [256] 
Multiscale mechanism  Images of multiple scales are input and the results are fused so that the final output combines the features from receptive fields of different sizes.  MUNet [257]; MDFANet [258] 
Ensemble mechanism  A group of neural networks processes the same input data in parallel and then combines their outputs to complete the segmentation.  AssemblyNet [259]; DIUNet [260] 
Dilated mechanism  The dilated convolution is used in the encoder and decoder to increase the size of the small convolution kernel while keeping the parameter amount of the convolution unchanged.  DiSegNet [261]; DIN [262] 
Attention mechanism  Attention modules are added to the encoder, decoder, and skip connections.  ANUNet [263]; SAUNet [264] 
Transformer mechanism  The operation of the encoder, decoder, and jump connection is changed to transformer.  TANet [265]; FATNet [266] 
Author  Variation  Year  Features 

Ronneberger et al. [212]  UNet  2015  It consists of a contracting path and an expanding path, which are used to obtain contextual information and precise localization, respectively. These two paths are symmetrical to each other. 
Çiçek et al. [267]  3D UNet  2016  The core structure still contains a contracting path and a symmetric expanding path. Overall, 3D volume segmentation is supported and all 2D operations are replaced by corresponding 3D operations, resulting in 3D segmented images that can be segmented with minimal annotation examples. 
Oktay et al. [268]  Attention UNet  2018  It is a hybrid structure that uses attention gates to focus on specific objects of importance and each level in the expansion path has an attention gate through which corresponding features from the contraction path must pass. 
Alom et al. [269]  R2UNet ^{1}  2018  It builds on residual and loop techniques, using a simpler concatenation of functions from the encoder to decoder. 
Zhou et al. [270]  UNet++  2018  It is essentially a deeply supervised encoder–decoder network, where the encoder and decoder subnetworks are connected by a series of nested, dense skip pathways, aiming to reduce the semantic gap between feature maps. 
Khanna et al. [255]  Residual UNet  2020  Residuals are integrated into the contraction path of UNet networks, thus reducing the computational burden and avoiding network degradation problems. 
Ibtehaz et al. [271]  MultiResUNet  2020  Res paths are proposed to reconcile two incompatible sets of features. The two feature mappings are more uniform. MultiRes blocks are proposed to augment UNet’s multiresolution analysis capabilities. It is lightweight and requires less memory. 
Li et al. [263]  ANUNet ^{2}  2020  A redesigned dense skip connection and attention mechanism are introduced and a new hybrid loss function is designed. 
Zhang et al. [260]  DIUNet ^{3}  2020  The InceptionRes module and the densely connecting convolutional module are integrated into the Unet structure. 
Yeung et al. [272]  Focus UNet  2021  Efficient spatial and channel attention are combined into Focus Gate. Focus Gate uses an adjustable focal parameter to control the degree of background suppression. Shortrange skip connections and deep supervision are added. Hybrid focal loss is used to deal with classimbalanced image segmentation. 
Beeche et al. [273]  Super UNet  2022  In the classical UNet architecture, a dynamic receiving field module and a fusion upsampling module are integrated. Image segmentation performance can be improved significantly by dynamic receptive field and fusion up sampling. 
Author  Datasets  IoU (%)  Dice (%)  Precision (%)  Recall (%)  DSC ^{1} (%)  JI ^{2} (%)  Body Part or Organ 

Ronneberger et al. [212]  LiTS  89.49  94.45  93.24  95.70      Liver 
CHAOS  84.46  91.58  89.20  94.08      Kidney  
CHAOS  76.18  86.48  82.34  91.06      Spleen  
CHAOS  75.37  85.96  87.31  84.65      Liver  
Çiçek et al. [267]  Private dataset  86.30            The Xenopus kidney 
Oktay et al. [268]  LiTS  93.39  96.58  96.79  96.37      Liver 
CHAOS  85.77  92.34  90.97  93.76      Kidney  
CHAOS  84.13  91.38  91.54  91.22      Spleen  
CHAOS  76.00  86.37  91.11  82.09      Liver  
Alom et al. [269]  LiTS  90.69  95.11  93.80  96.48      Liver 
CHAOS  85.54  92.21  91.92  92.50      Kidney  
CHAOS  81.50  89.77  93.60  86.24      Spleen  
CHAOS  77.80  87.50  92.11  83.39      Liver  
Khanna et al. [255]  LUNA16        98.61 ± 0.14  98.63 ± 0.05  97.32± 0.10  Lung 
VESSEL12        99.61 ± 0.01  99.62 ± 0.003  99.24 ± 0.007  Lung  
HUGILD        98.73 ± 0.001  98.68 ± 0.04  97.39 + 0.06  Lung  
Zhou et al. [270]  LiTS  94.46  97.15  98.16  96.17      Liver 
CHAOS  86.58  92.81  90.87  94.82      Kidney  
CHAOS  81.05  89.53  86.37  92.93      Spleen  
CHAOS  84.23  91.39  93.06  89.79      Liver  
Yeung et al. [272]  KvasirSEG  0.845 (mIoU)    91.70  91.60  0.910(mDSC)    colorectum 
CVCClinicDB  0.893 (mIoU)    93.00  95.60  0.941(mDSC)    colorectum  
Ibtehaz et al. [271]  ISIC2018            80.2988 ± 0.3717  Skin lesions 
CVCClinicDB            82.0574 ± 1.5953  Colon  
FMID ^{3}            91.6537 ± 0.9563  U2OS cells; NIH3T3 cells  
BraTS17            78.1936 ± 0.7868  Glioblastoma; lower grade glioma  
Li et al. [263]  LiTS  97.48  98.15  98.15  99.31      Liver 
CHAOS  90.10  94.79  94.00  95.60      Kidney  
CHAOS  89.23  94.31  95.19  93.44      Spleen  
CHAOS  87.89  93.55  94.23  92.88      Liver  
Zhang et al. [260]  KDSB2017 ^{4}    98.57          Lung 
DRIVE + STARE + CHASH_DB1    95.82          Blood vessel  
MICCAI BraTS 2017    98.92          Brain tumor  
Beeche et al. [273]  DRIVE (n = 40)          80.80 ± 0.021    Retinal vessels 
KvasirSEG (n = 1000)          80.40 ± 0.239    GI polyps  
CHASE DB1 (n = 28)          75.20 ± 0.019    Retinal vessels  
ISIC (n = 200)          87.70 ± 0.135    Skin lesions 
Method  Description  Typical Method  Features 

Instance  Samples from different source tasks are collected and reused for learning of the target task.  TrAdaBoost  The method is simple, easy to implement, unstable, and more empirical. 
Featurerepresentation  By introducing the source data features to help complete the learning task of the target data feature domain, the features of the source domain and the target domain are transformed into the same space through feature transformation.  Selftaught learning, multitask structure learning  Applicable to most methods, the effect is better, it is difficult to solve, and it is prone to overfitting. 
Parameter  When some parameters are shared between the source task and the target task, or the prior distribution of model hyperparameters is shared, the model of the source domain is transferred to the target domain.  Learning to learn, Regularized multitask learning  The similarity between the models can be fully utilized and the model parameters are not easy to converge. 
Relationalknowledge  It facilitates learning tasks on target data by mining relational patterns relevant to the target data from the source domain.  Mapping  Compatible for data with dependency and identical distribution 
Explanation Type  Characteristics 

Local  Provide explanations for individual samples. 
Global  Provide explanations for a set of samples or the entire model. 
Data Modality Specific  Explanation methods that apply only to specific data types. 
Data Modality Agnostic  Explanation methods are applicable to any data type. 
AdHoc  The model itself is designed to be inherently explainable. 
PostHoc  Provide explanations after classification is performed. 
Model Agnostic  Can explain any model and is not limited to a certain type. 
Model Specific  Only available on certain models. 
Attribution  Attempts to compute the most important neural network inputs relative to the network result. 
NonAttribution  Develop and validate an explainability method for a given specialized problem. 
Method  Year  Goal  Description  Features 

Xavier initialization [323]  2010  Solve the problem of gradient disappearance or gradient explosion that may be caused by random initialization.  The range of weight initialization is determined according to the number of input neurons in the previous layer and the number of output neurons in the next layer.  It reduces the probability of gradient vanishing/exploding problems. The influence of the activation function on the output data distribution is not considered and the ReLU activation function does not perform well. 
Orthogonal Initialization [324]  2013  Orthogonalize the weight matrix.  It solves the problem of gradient disappearance and gradient explosion under the deep network and is often used in RNN.  It can effectively reduce the redundancy and overfitting in the neural network and improve the generalization ability and performance of the network. Computational complexity is high, so it may not be suitable for large neural networks. 
He initialization [325]  2015  The input and output data have the same variance; suitable for neural networks using the ReLU activation function.  In the ReLU network, it is assumed that half of the neurons in each layer are activated and the other half is 0, just divide by 2 on the basis of Xavier to keep the variance constant.  Simple and effective, especially suitable for the case where the activation function is ReLU. Compared with the Xavier initialization, it can effectively improve the training speed and performance of the network. In some cases, it may cause the weight to be too small or too large, thus affecting the network performance. 
Datadependent Initialization [326]  2015  Focus on behavior on smaller training sets, handle structured initialization, and improve pretrained networks.  It relies on the initialization process of the data. All units in the network train at roughly the same rate by setting the network’s weights.  CNN representations for tasks with limited labeled data are significantly improved and representations learned by selfsupervised and unsupervised methods are improved. Early training of CNNs on largescale datasets is greatly accelerated. 
LSUV [327]  2015  Produces thin and very deep neural networks.  The weights of each convolutional or inner product layer are preinitialized with an orthogonal matrix. The variance in the output of each layer is normalized to be equal to 1 from the first layer to the last layer.  It has minimal computation and very low computational overhead. Due to variability in the data, it is often not possible to normalize the variance with the required precision. 
Sparse initialization [328]  2017  Achieving sparsity.  The weights are all initialized to 0. Some parameters are chosen randomly with some random values.  The parameters occupy less memory. Redundancy and overfitting in neural networks can be reduced. The generalization ability and performance of the network are improved. Some elements in the weight matrix may be too large or too small, thereby affecting the performance of the network. 
Fixup [329]  2019  For networks with residual branches.  Standard initialization is rescaled appropriately.  Deep residual networks can be reliably trained without normalization. 
ZerO Initialization [330]  2021  Deterministic weight initialization.  It is based on the identity transformation and the Hadamard transformation and only initializes the weights of the network with 0 and 1 (up to a normalization factor).  Ultradeep networks without batch normalization are trained. It has obvious characteristics of lowrank learning and solves the problem of training decay. The trained model is more reproducible. 
Data Augmentation Category  Advantages  Disadvantages 

Geometric transformations  It is simple and easy to implement, which can increase the spatial geometry information of the data set and improve the robustness of the model in different perspectives and positions.  The amount of added information is limited. The data is repeatedly memorized. Inappropriate operations may change the original semantic annotation of the image. 
Color space  The method is simple and easy to implement. The color information of the dataset is added to improve the robustness of the model under different lighting conditions.  The amount of added information is limited and repeated memory of the data may change the important color information in the image. 
Kernel filter  It can improve the robustness of the model to motion blur and highlight the details of objects.  It is implemented by filtering, which is repeated with the internal mechanism of CNN. 
Image Erasing  It can increase the robustness of the model under occlusion conditions, enable the model to learn more descriptive features in the image, and pay attention to the global information of the entire image.  The semantic information of the original image may be tampered with. Images may not be recognized after important partial information is erased. 
Mixing images  The pixel value information of multiple images is mixed.  Lack of interpretability 
Noise injection  This method enhances the filtering ability of the model to noise interference and redundant information and improves the recognition ability of the model of different quality images.  Unable to add new valid information. The improvement effect on model accuracy is not obvious. 
Feature space augmentation  The feature information of multiple images is fused.  Vector data is difficult to interpret. 
Adversarial training [339]  It can improve the weak links in the learned decision boundary and improve the robustness of the model.  Extremely slow and inaccurate. More data, deeper and more complex models are required. 
GANbased Data Augmentation  Sampling from the fitted data distribution generates an unlimited number of samples.  A certain number of training samples are required to train the GAN model, which is difficult to train and requires extra model training costs. In most cases, the quality of the generated images is difficult to guarantee and the generated samples cannot be treated as real samples. 
Neural style transfer  It can realize mutual conversion between images of the same content and different modalities and can help solve special problems in many fields.  Two datasets from different domains need to be constructed to train the style transfer model, which requires additional training overhead. 
Meta learning data augmentations  Neural network is used to replace the definite data augmentation method to train the model to learn better augmentation strategies.  Introducing additional networks requires additional training overhead. 
Reinforcement learning data augmentation  Combining existing data augmentation methods to search for the optimal strategy.  The policy search space is large, the training complexity is high, and the calculation overhead is large. 
Data Augmentation Category  Method  Year  Describe  M/D 

Geometric transformations  Flipping    Usually, the image flip operation is performed about the horizontal or vertical axis.  M 
Rotating    Select an angle and rotate the image left or right to change the orientation of the image content.  M  
Zoom    The image is enlarged and reduced according to a certain ratio without changing the content in the image.  M  
shearing    Move part of the image in one direction and another part in the opposite direction.  M  
translating,    Shifting the image left, right, up, or down avoids positional bias in the data.  M  
skew    Perspective transforms.  M  
Cropping    It is divided into uniform cropping and random cropping. Uniform cropping crops images of different sizes to a set size. Random cropping is similar to translation, the difference is that translation retains the original image size and cropping reduces the size.  M  
Color space  Color jittering [213]  2016  Randomly change the brightness, contrast, and saturation of the image.  M 
PCA jittering [340]  2017  Principal component analysis is carried out on the image to obtain the principal component and then the principal component is added to the original image by Gaussian disturbance with mean of 0 and variance of 0.1 to generate a new image.  M  
Kernel filter  Gaussian blur filter [341]  1991  The image is blurred using Gaussian blur.  M 
edge filter [342]  1986  Get an image with edge sharpening, highlighting more details of the object.  M  
PatchShuffle [343]  2017  Rich local variations are created by generating images and feature maps with internally disordered patches.  M  
Image Erasing  Random erasing [344]  2020  During training, a rectangular region in the image is randomly selected and its pixels are erased with random values.  M 
Cutout [345]  2017  Randomly mask input square regions during training.  M  
HaS [346]  2017  Patches are hidden randomly in training images. When the most discriminative parts are hidden, forcing the network to find other relevant parts.  M  
GridMask [347]  2020  Based on the deletion of regions in the input image, the deleted regions are a set of spatially uniformly distributed squares that can be controlled in terms of density and size.  M  
FenceMask [348]  2020  The “ simulation of object occlusion” strategy is employed to achieve a balance between the information preservation of input data and object occlusion.  M  
Mixing images  Mixup [349]  2017  The neural network is trained on convex combinations of pairs of examples and their labels.  M 
SamplePairing [350]  2018  Another image randomly selected from the training data is superimposed on one image to synthesize a new sample, that is, the average value of the two images is taken for each pixel.  M  
BetweenClass Learning [351]  2018  Two images belonging to different classes are mixed in a random ratio to generate betweenclass images.  M  
CutMix [352]  2019  Patches were cut and pasted among the training images, where the ground truth labels was also mixed in proportion to the area of the patches.  M  
AugMix [353]  2019  Using random and diverse augmentation, Jensen–Shannon divergence consistency loss, and mixing multiple augmented images  M  
Manifold Mixup [354]  2019  Using semantic interpolation as an additional training signal, neural networks with smoother decision boundaries at multiple representation levels are obtained.  M  
Fmix [355]  2020  A mixed sample data augmentation using a random binary mask obtained by applying thresholds to lowfrequency images sampled from Fourier space.  M  
SmoothMix [356]  2020  Image blending is conducted based on soft edges and training labels are computed accordingly.  M  
Deepmix [357]  2021  Takes embeddings of historical samples as input and generates augmented embeddings online.  M  
SalfMix [358]  2021  A data augmentation method for generating saliency mapbased selfmixing images.  M  
Noise injection  forward noise adjustment scheme [359]  2018  Insert random values into an image to create a new image.  M 
DisturbLabel [360]  2016  Randomly replace the labels of some samples during training and apply disturbance to the sample labels, which is equivalent to adding noise at the loss level.  M  
Feature space augmentation  Dataset Augmentation in Feature Space [361]  2017  Representation is first learned using encoder–decoder algorithm and then different transformations are applied to the representation, such as adding noise, interpolation, or extrapolation.  D 
Feature Space Augmentation for LongTailed Data [362]  2020  Use features learned from the classes with sufficient samples to augment underrepresented classes in the feature space.  D  
SMOTE [363]  2002  Interpolate on the feature space to generate new samples.  D  
FeatMatch [364]  2020  A learned featurebased refinement and augmentation method that exploits information from withinclass and acrossclass representations extracted through clustering to produce various complex sets of transformations.  D  
Adversarial training [339]  FGSM [365]  2014  This method believes that the attack is to add disturbance to increase the loss of the model and it should be best to generate attack samples along the gradient direction. It is a onetime attack. That is, adding a gradient to a graph only increases the gradient once.  D 
PGD [366]  2017  As the strongest firstorder adversary, it is an efficient solution to the internal maximization problem.  D  
FGSM+ random initialization [367]  2020  Eric Wong, Leslie Rice, and J. Zico Kolter. Fast is better than free: Revisiting adversarial training. In ICLR, 2020. Adversarial training using FGSM, combined with random initialization, is as effective as PGDbased training, but at a much lower cost.  D  
GradAlign [368]  2020  Catastrophic overfitting is prevented by explicitly maximizing the gradient alignment inside the perturbation set.  D  
Fast C and W [369]  2021  An accelerated SARTR AA algorithm. A network of trained deep encoder replaces the process of iteratively searching for the best perturbation of the input SAR image in the vanilla C and W algorithm.  D  
GANbased Data Augmentation  GAN [194]  2014  sing GAN generative models to generate more data can be used as an oversampling technique to address class imbalance.  D 
CGAN [370]  2014  Add some constraints to GAN to control the generation of images.  
DCGAN [371]  2015  Combining CNN with GAN, deep convolutional adversarial pair learns representation hierarchies from object parts to scenes in the generator and discriminator.  D  
LapGAN [372]  2015  Images are generated in a coarsetofine fashion using a cascade of convolutional networks within the Laplacian Pyramid framework.  D  
InfoGAN [373]  2016  Interpretable representation learning in a completely unsupervised manner via informationmaximizing GANs.  D  
EBGAN [374]  2016  An energybased generative adversarial network model in which the discriminator is treated as a function of energy.  D  
WGAN [375]  2017  The loss function is derived by means of earth mover or Wasserstein distance.  D  
BEGAN [376]  2017  The generator and discriminator are balanced for training an autoencoderbased generative adversarial network.  D  
PGGAN [377]  2017  Generators and discriminators are progressively increasing.  D  
BigGAN [378]  2018  Largescale GAN training for highfidelity natural image synthesis based on GAN architecture.  D  
StyleGAN [379]  2019  A stylebased GAN generator architecture controls the image synthesis process.  D  
SiftingGAN [380]  2019  The traditional GAN framework is extended to include an online output method for generating samples, a generative model screening method for model sifting, and a labeled sample discrimination method for sample sifting.  D  
Neural style transfer  CycleGAN [381]  2017  It only needs to build the respective sample sets of the two image style domains, and unpaired samples can be used for training, which greatly reduces the difficulty of building training sample sets and makes the style transfer between any image domains easier to realize.  D 
Pix2Pix [382]  2017  Conditional adversarial networks as a general solution to imagetoimage translation problems.  D  
Meta learning data augmentations  Neural augmentation [383]  2017  Before the classification network, an augmented network is introduced to input two randomly selected images of the same category, learn the common content information or style information of the two images through the neural network, and then obtain an “enhanced image”, which is input into the classification network together with the original image for classification model training.  D 
Smart Augmentation [384]  2017  Reduce network losses by creating a network to learn how to generate enhanced data during the training of the target network.  D  
Reinforcement learning data augmentation  AutoAugment [385]  2018  The search space is designed and has a strategy consisting of many substrategies, the best data augmentation is found by an automatic search strategy.  D 
Fast Autoaugment [386]  2019  The efficient search strategy based on density matching is used to find better data expansion, thus reducing the time of high order training.  D  
Faster AutoAugment [387]  2020  The differentiable policy search pipeline not only estimates the gradient for many conversion operations with discrete parameters but also provides an efficient mechanism for selecting operations.  D  
MARL [388]  2021  An automatic local patch augmentation method based on multiagent collaboration and the first to use reinforcement learning to find a patchlevel data augmentation strategy.  D  
RandAugment [389]  2020  Simplifies the search space, greatly reduces the computational cost of automatic augmentation, and allows the removal of a separate proxy task.  D 
Reference  Year  Method  Dataset (s)  Imaging Modality  Type of Cancer  Evaluation Metric(s) 

Kavitha et al. [402]  2021  BPNN  MiniMIAS DDSM  Mammography  Breast Cancer  Accuracy: 98.50% Accuracy: 97.55% 
Nawaz et al. [399]  2018  DenseNet  BreakHis  histopathological images  Breast Cancer  Accuracy: 95.4% 
Spanhol et al. [403]  2016  CNN  BreaKHis  Histopathology  Breast cancer  Accuracy: between 98.87% and 99.34% for the binary classification; between 90.66% and 93.81% for the multiclass classification. 
Fu’adah et al. [391]  2020  CNN  ISIC    skin cancer  Accuracy: 99% 
Anand et al. [393]  2022  DCELM  private  histopathological images  Bone Cancer  Accuracy: 97.27% Sensitivity: 98.204% Specificity: 99.568% Precision: 87.832% 
Beevi et al. [394]  2017  DBN  MITOS RCC  histopathology images  Breast cancer  Fscore: 84.29% Fscore: 75% 
Shahweli [395]  2020  DBN  IARC TP53    lung cancer  Accuracy: 96% 
AbdelZaher et al. [397]  2016  DBN  WBCD    Breast cancer  Accuracy: 99.68% Sensitivity: 100% Specificity: 99.47% 
Jabeen et al. [400]  2022  DarkNet53  BUSI  ultrasound images  Breast cancer  Accuracy: 99.1% 
Das et al. [404]  2019  DNN  Private dataset  CT/3D  Liver  Accuracy: 99.38% Jaccard index: 98.18% 
Mohsen et al. [405]  2018  DNN  Harvard Medical School website  MRI  Brain  Precision: 97% Rate: 96.9% Recall: 97% Fmeasure: 97% AUC: 98.4% 
ElGhany et al. [401]  2023  ResNet101  LC25000  histopathological images  Lung and colon cancer  Precision99.84% Recall: 99.85% F1score: 99.84% Specificity: 99.96% Accuracy: 99.94% 
Attallah [406]  2023  CerCan·Net  SIPaKMeD and Mendeley    Cervical cancer  Accuracy: 97.7% (SIPaKMeD) Accuracy: 100% (Mendeley) 
Reference  Year  Method  Imaging Modality  Type of Cancer  Datasets  Evaluation Metrics 

Shen et al. [420]  2021  GMIC  Mammogram  Breast Cancer  NYUBCS + CBISDDSM  DSC ^{1} (Malignant): 0.325 ± 0.231 DSC (Benign): 0.240 ± 0.175 PxAP ^{2} (Malignant): 0.396 ± 0.275 PxAP (Benign): 0.283 ± 0.244 
Ranjbarzadeh et al. [421]  2021  CConvNet/CCNN  MRI  Brain tumor  BRATS 2018  Dice (mean): 0.9203 (Whole ^{3}) 0.9113 (Enh ^{4}), 0.8726 (Core ^{5}) Sensitivity (mean): 0.9386 (Whole), 0.9217 (Enh), 0.9712 (Core) HAUSDORFF99 (mm): 1.427 (Whole), 1.669 (Enh), 2.408 (Core) 
Ari et al. [426]  2018  ELMLRF  MRI  Brain Cancer  Simulated datasets  Accuracy: 97.18% Sensitivity: 96.80% Specificity: 97.12% 
Zhang et al. [414]  2022  Mask RCNN  MRI  Breast Cancer  DCEMRI  Accuracy (mean): 0.86 DSC: 0.82 
Asuntha et al. [419]  2020  FPSOCNN  CT  Lung cancer  WRT ^{6}  Average accuracy: 94.97% Average sensitivity: 96.68% Average specificity: 95.89% 
LIDC  Average accuracy: 95.62% Average sensitivity: 97.93% Average specificity: 6.32%  
Zhou et al. [418]  2019  3D CNN  MRI  Breast cancer  Private dataset  Accuracy: 83.7% Sensitivity: 90.8% Specificity: 69.3% Overall dice distance: 0.501 ± 0.274 
Welikala et al. [411]  2020  ResNet101 + Faster RCNN  MRI  Oral cancer  Private dataset  F1: 87.07% (for identification of images that contained lesions) F1: 78.30% (for the identification of images that required referral) 
Zhang et al. [424]  2022  DDTNet  Histopathological image  Breast cancer  BCalym  F1: 0.885 Dice: 0.845 PQ ^{7}: 0.731 Time: 0.0674 
PostNATBRCA  F1: 0.892 Dice: 0.846 PQ: 0.782 Time: 0.0662  
TCGAlym  F1: 0.793 Dice: 0.788 PQ: 0.635 Time: 0.0647  
Maqsood et al. [425]  2022  TTCNN  Mammogram  Breast cancer  DDSM + INbreast + MIAS  Accuracy: 97.49% 
Chattopadhyay et al. [427]  2022  CNN  MRI  Brain cancer  BRATS  Accuracy: 99.74% 
Luo et al. [422]  2022  SCPMNet  3D CT  Lung cancer  LUNA16  Average sensitivity: 89.2% 
Cao et al. [413]  2019  Mask RCNN  Pathological images  Gastric Cancer  Private dataset  AP: 61.2 
Reference  Year  Method  Datasets  Imaging Modality  Type of Cancer  Evaluation Metrics 

Zhu et al. [439]  2018  Adversarial FCNCRF  Inbreast + DDSMBCRP  Mammogram  Breast Cancer  Accuracy: 97.0% 
AlAntari et al. [440]  2018  Frcn  Inbreast  Mammogram  Breast Cancer  Dice: 92.69% MCC ^{1}: 85.93% Accuracy: 92.97% JSC ^{2}: 86.37% 
Dong et al. [435]  2020  HFCNN  Private dataset  CT  Liver cancer  Dice: 92% 
Shukla et al. [436]  2022  Cfcns  3DIRCAD  CT  Liver cancer  Accuracy: 93.85% 
Ayalew et al. [438]  2021  UNet  3Dircadb01 + LITS  CT  Liver cancer  Dice: 96% (liver segmentation) Dice: 74% (segmentation of tumors from the liver) Dice: 63% (segmentation of tumor from abdominal CT scan images) 
Li et al. [441]  2018  CRUNet  Inbreast DDSMBCRP  Mammogram  Breast Cancer  Dice Index: 93.66% (Inbreast) Dice Index: 91.43% (DDSMBCRP) 
Shen et al. [442]  2019  RescuNet + MSrescuNet  Inbreast  Mammogram  Breast Cancer  Dice: 91.78% Jaccard index: 85.12% Accuracy: 94.16% 
Li et al. [443]  2019  UNet + AGS  DDSM  Mammogram  Breast Cancer  Accuracy: 78.38% Sensitivity: 77.89% Fscore: 82.24% 
Hossain [444]  2019  UNet  DDSM  Mammogram  Breast Cancer  Dice: 97.80% Fscore: 98.50% Jaccard index: 97.4% 
Sun et al. [445]  2020  Aunet  INbreast CBISDDSM  Mammogram  Breast Cancer  Dice: 79.10% (INbreast) Dice: 81.80% (CBISDDSM) 
Min et al. [446]  2020  Mask RCNN  Inbreast  Mammogram  Breast Cancer  Dice: 88.00% 
AlAntari et al. [447]  2020  FrCN  Inbreast  Mammogram  Breast Cancer  Dice: 92.69% Accuracy: 92.97% MCC: 85.93% JAC: 86.37% 
Abdelhafiz et al. [448]  2020  Vanilla UNet  Inbreast + DDSM  Mammogram  Breast Cancer  Accuracy (mean): 92.6% IoU ^{3}: 90.90% 
Rajalakshmi et al. [449]  2020  DSUNet  Inbreast CBISDDSM  Mammogram  Breast Cancer  Dice: 79% (Inbreast) Sensitivity: 81% (Inbreast) Dice: 82.7% (CBISDDSM) Sensitivity: 84.1% (CBISDDSM) 
Saffari et al. [450]  2020  Cgan  Inbreast  Mammogram  Breast Cancer  Dice: 88.0% Jaccard index: 78.0% Accuracy: 98.0% Precision: 97.85% Sensitivity: 97.85% Specificity: 99.28% 
Singh et al. [451]  2020  Cgan  DDSM  Mammogram  Breast Cancer  Dice: 94.0% IoU: 87.0% 
Ahmed et al. [452]  2020  Mask RCNN Deep lab v3 