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Algorithms
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Machine Learning in Medical Signal and Image Processing (2nd Edition)
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Machine Learning in Medical Signal and Image Processing (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1531

Special Issue Editor

Dr. Maryam Ravan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, New York Institute of Technology (NYIT), NYC Campus, Room 810, 1855 Broadway, New York, NY 10023-7692, USA
Interests: signal processing; machine learning; biomedical engineering; microwave imaging; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research focused on developing and applying machine learning algorithms for medical applications to this Special Issue, “Machine Learning in Medical Signal and Image Processing (2nd Edition)”. We are looking for new and innovative machine learning approaches with medical applications. Potential applications include, but are not limited to, biomedical signal processing, biomedical image processing, biosensors, bioinformatics and computational biology, neural and rehabilitation engineering, cardiovascular engineering, therapeutic and diagnostic systems, robotics, clinical engineering, healthcare information systems and telemedicine, devices and technologies, and emerging topics in biomedical engineering.

Dr. Maryam Ravan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • disease classification and prognosis prediction
  • deep learning (CNN, RNN, GAN, etc.) in brain–computer interface (BCI) and medical images
  • radiological image processing (MRI, fMRI, CT scan, PET, ultrasound, X-ray, etc.)
  • clinical data processing (electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), etc.)
  • data fusion techniques
  • statistical pattern recognition
  • advanced artifact reduction
  • wearable sensors
  • virtual reality

Related Special Issue

Published Papers (2 papers)

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Research

17 pages, 2655 KiB  
Article
Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals
by Kyle McMillan, Rosa Qiyue So, Camilo Libedinsky, Kai Keng Ang and Brian Premchand
Algorithms 2024, 17(4), 156; https://doi.org/10.3390/a17040156 - 12 Apr 2024
Viewed by 581
Abstract
Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous [...] Read more.
Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))
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12 pages, 941 KiB  
Article
Challenges in Reducing Bias Using Post-Processing Fairness for Breast Cancer Stage Classification with Deep Learning
by Armin Soltan and Peter Washington
Algorithms 2024, 17(4), 141; https://doi.org/10.3390/a17040141 - 28 Mar 2024
Viewed by 608
Abstract
Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the [...] Read more.
Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the training data. We quantified the bias of models trained to predict breast cancer stage from a dataset consisting of 1000 biopsies from 842 patients provided by AIM-Ahead (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). Notably, the majority of data (over 70%) were from White patients. We found that prior to post-processing adjustments, all deep learning models we trained consistently performed better for White patients than for non-White patients. After model calibration, we observed mixed results, with only some models demonstrating improved performance. This work provides a case study of bias in breast cancer medical imaging models and highlights the challenges in using post-processing to attempt to achieve fairness. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))
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