Deep Learning in Image Analysis: Progress and Challenges

Dear Colleagues,

Due to its huge advantages, deep-learning-related methods have become the mainstream technology in image analysis, making significant progress and holding vast prospects. They are applied in a variety of tasks, such as image classification, object segmentation, object detection, image registration, image fusion, biomedical engineering, natural language processing, etc. Among different kinds of deep learning techniques, supervised learning was first adopted. Later, unsupervised, semi-supervised, few-shot, one-shot, and zero-shot learning methods received extensive attention. For the network structure, convolutional neural networks, recurrent neural networks, generative networks, attention mechanisms, and transformers have been designed and widely applied.

Which advancements will eventually be more productive and innovative in this field?

We request contributions presenting techniques (methods, tools, ideas, or even market evaluations) that will contribute to the future roadmap of deep learning, as well as concepts for significantly innovative objectives in image analysis techniques. This Special Issue will discuss the novel supervised, unsupervised, semi-supervised, few-shot, etc., deep learning methods in image analysis, including classification, segmentation, detection, registration, domain generalization, multi-modality fusion, etc. Scientifically founded innovative and speculative research lines are welcome for proposal and evaluation.

Prof. Dr. Yanfeng Li
Guest Editor

Manuscript Submission Information

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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. Journal of Imaging 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 1800 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.

• deep learning
• image classification
• image segmentation
• object detection
• image registration
• image fusion
• multi-modality
• domain generalization
• unsupervised learning
• semi-supervised learning
• few-shot learning