Recent Advances in Medical Image Classification
This is an incremental review paper summarizing existing methods for medical image classification, which is crucial for diagnosis and treatment.
The paper reviews recent progress in medical image classification, highlighting advances in deep learning models like CNNs and Vision Transformers to address limited labeled data and enhance predictive results with Explainable AI.
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited labeled data, and enhance and explain predictive results through Explainable Artificial Intelligence.