Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and Impact

Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and Impact | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 December 2025 V1 Latest version Share on Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and Impact Authors : Mahade Hasan 0009-0006-2778-1216 [email protected] , Farhana Yasmin , and Shugufta Fatima Authors Info & Affiliations https://doi.org/10.22541/au.176581998.82900297/v1 222 views 121 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Deep learning (DL) has reshaped the landscape of medical imaging by enabling automated, scalable, and high-accuracy analysis across diverse clinical modalities including MRI, CT, PET/SPECT, ultrasound, Xray, and digital pathology. Early convolutional neural networks (CNNs) established the foundation for tasks such as segmentation, detection, classification, and registration by learning hierarchical spatial representations directly from raw images. Subsequent architectural innovations particularly attention mechanisms, hybrid CNN-attention models, and transformer-based encoders have significantly improved the modeling of long-range dependencies, multi-scale anatomical structures, and complex clinical patterns. Neural architecture search (NAS) has further advanced the field by automating the design and optimization of medical AI systems, leading to architectures that are more efficient, adaptive, and robust. Parallel progress in self-supervised learning and generative modeling, including masked autoencoding, contrastive learning, GANs, VAEs, and diffusion models, has reduced the dependence on large annotated datasets while improving representation quality, modality translation, superresolution, and data augmentation. Multi-modal fusion techniques now enable the integration of imaging, clinical variables, and genomics, supporting more holistic and patient-specific decision making. However, significant challenges persist in clinical deployment, including domain shift, acquisition variability, data imbalance, uncertainty quantification, fairness, out-of-distribution detection, and regulatory requirements for safety and interpretability. This chapter presents a comprehensive overview of these advances, covering foundational principles, architectural evolution, generative and self-supervised paradigms, robustness and generalization strategies, and applications across neuroimaging, oncology, cardiology, abdominal and gastrointestinal imaging, ophthalmology, and pathology. Finally, emerging directions such as foundation models, federated learning, harmonization, and digital-twin frameworks are discussed as key enablers of next generation medical AI systems that are reliable, equitable, and clinically meaningful. Supplementary Material File (advances_in_deep_learning_for_medical_imaging.pdf) Download 12.02 MB Information & Authors Information Version history V1 Version 1 15 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cnn deep learning generative models medical imaging neural architecture search Authors Affiliations Mahade Hasan 0009-0006-2778-1216 [email protected] School of Software, Nanjing University of Information Science and Technology View all articles by this author Farhana Yasmin School of Computer Science, Nanjing University of Information Science and Technology View all articles by this author Shugufta Fatima Stanley College of Engineering and Technology for Women View all articles by this author Metrics & Citations Metrics Article Usage 222 views 121 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mahade Hasan, Farhana Yasmin, Shugufta Fatima. Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and Impact. Authorea . 15 December 2025. DOI: https://doi.org/10.22541/au.176581998.82900297/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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