Deep Learning for Medical Image Analysis: A Comprehensive Review
Abstract
As deep learning continues to develop at a fast pace, the field of medical imaging has also seen significant changes due to new technologies making it possible to automatically analyse large amounts of complex image data with much greater accuracy and efficiency than ever before. In this chapter, we will describe and examine many different deep-teaching techniques used in various imaging modalities such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), X-ray, Ultrasound, Mammograms, and Dermatoscopic as well as Digital Pathology images. We will also discuss several key deep-teaching architectures including CNN (Convolutional Neural Networks), ViT (Vision Transformer), and GAN (Generative Adversarial Networks), and how these architectures can be used in the performance of standard imaging tasks including Image Classification, Object Detection, Image Segmentation, and Image Registration. Moving forward, from the years 2023 through 2025 there will be greater use of Foundation Models, Multimodal Large Language Models, Diffusion-based Data Generation, Real-time Surgical AI, Explainable Transformers, and Federated Learning for training with Privacy Assurance. Finally, we will provide insight into potential future areas for research including Developing Universal Vision-Language Models, AI-driven Precision Medicine, Developing Generalizable Algorithms, Integration into Clinical Settings, and the Importance of Human-AI Collaboration.
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