综述医学图像分类最新进展，涵盖从基础到应用的多层级方法。
Recent Advances in Medical Image Classification
- 基于卷积神经网络与视觉变换器的深度学习方法
- 融合视觉语言模型提升小样本场景下的分类性能
- 引入可解释AI增强结果可信度，适合医疗AI研究者
医学图像分类对诊断与治疗至关重要，近年来在人工智能推动下取得显著进展。本文综述该领域近期成果，聚焦三个层次的解决方案：基础、特定任务及实际应用。重点介绍传统方法中卷积神经网络与视觉变换器的应用进展，以及基于视觉语言模型的前沿技术。这些方法有效缓解标注数据稀缺问题，并通过可解释人工智能技术提升预测结果的透明度与可信度。
原文摘要 · Abstract (English)
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.
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