Deep Learning for Medical and Remote Sensing Image Analysis

Deep Learning for Medical and Remote Sensing Image Analysis

This community develops deep learning algorithms to detect, segment, and reconstruct images from medical scans and satellite sensors. The work is primarily applied to diagnostic imaging, such as identifying brain tumors and diabetic retinopathy, and to remote sensing tasks like target detection and image fusion.

The research relies heavily on convolutional neural networks, generative adversarial networks, and diffusion models. Recurring methods include data augmentation, transfer learning, and image enhancement. Specific applications named in the literature include computed tomography, magnetic resonance imaging, and hyperspectral imaging. The community also addresses the mathematical foundations of image reconstruction, with a significant number of papers focusing on the Radon transform and Fourier transform. These techniques are applied to solve inverse problems in both clinical diagnostics and environmental monitoring.

The largest share of the community's output is found in radon research, accounting for 8.4% of all radon research, and 1,669 papers here. This is the element with the highest paper count within this group.

The community comprises 6,576 papers, published most frequently in Optics & Laser Technology, Journal of Imaging Informatics in Medicine, and Journal of Imaging.

Recent work continues to focus on the clinical application of AI in radiology and pathology, as well as the fusion of optical and synthetic aperture radar images for remote sensing.

Papers behind this description

• Deep learning modelling techniques: current progress, applications, advantages, and challenges — Artificial Intelligence Review, 2023 — doi:10.1007/s10462-023-10466-8
• UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation — IEEE Transactions on Medical Imaging, 2024 — doi:10.1109/tmi.2024.3398728
• A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications — Journal Of Big Data, 2023 — doi:10.1186/s40537-023-00727-2
• Current applications and challenges in large language models for patient care: a systematic review — Communications Medicine, 2025 — doi:10.1038/s43856-024-00717-2
• A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation — Expert Systems with Applications, 2023 — doi:10.1016/j.eswa.2023.122778
• A review: Data pre-processing and data augmentation techniques — Global Transitions Proceedings, 2022 — doi:10.1016/j.gltp.2022.04.020
• Efficient Geometry-aware 3D Generative Adversarial Networks — 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 — doi:10.1109/cvpr52688.2022.01565
• Unsupervised Medical Image Translation With Adversarial Diffusion Models — IEEE Transactions on Medical Imaging, 2023 — doi:10.1109/tmi.2023.3290149
• The limits of fair medical imaging AI in real-world generalization — Nature Medicine, 2024 — doi:10.1038/s41591-024-03113-4
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications — IEEE Transactions on Knowledge and Data Engineering, 2021 — doi:10.1109/tkde.2021.3130191
• Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers — IEEE Access, 2024 — doi:10.1109/access.2024.3397775
• Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery — Clinics and Practice, 2025 — doi:10.3390/clinpract15090169
• Bridging the digital divide: artificial intelligence as a catalyst for health equity in primary care settings — International Journal of Medical Informatics, 2025 — doi:10.1016/j.ijmedinf.2025.106051
• A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches — Journal of Imaging, 2025 — doi:10.3390/jimaging11100326
• eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing — Sensors, 2025 — doi:10.3390/s25216649
• Edge AI in Practice: A Survey and Deployment Framework for Neural Networks on Embedded Systems — Electronics, 2025 — doi:10.3390/electronics14244877
• Adversarial artificial intelligence in radiology: Attacks, defenses, and future considerations — Diagnostic and Interventional Imaging, 2025 — doi:10.1016/j.diii.2025.05.006