Recent Advances in Medical Image Classification

TL;DR

This paper reviews recent progress in medical image classification, emphasizing deep learning, vision transformers, and explainability techniques that improve diagnostic accuracy and interpretability.

Contribution

It provides a comprehensive overview of recent AI-based methods across basic, specific, and applied levels in medical image classification.

Findings

Deep learning models like CNNs and Vision Transformers have advanced the field.

Vision Language Models address limited labeled data challenges.

Explainable AI enhances understanding of model predictions.

Abstract

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.