Medical Image Datasets

Medical image datasets are crucial for training and evaluating machine learning models used in disease diagnosis and treatment planning. Current research focuses on addressing challenges like data scarcity and imbalance through techniques such as data augmentation (including GANs and image translation), test-time training, and federated learning to improve model performance and generalization across diverse patient populations and imaging modalities. Convolutional neural networks (CNNs), transformers, and large multi-modal models are prominent architectures, often combined with techniques to enhance interpretability and mitigate biases. These advancements hold significant potential for improving the accuracy, efficiency, and accessibility of medical image analysis, ultimately leading to better patient care.