用生成模型做乳腺影像分类，效果优于传统方法。
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?
- 用分数驱动的生成模型当分类器，不依赖传统分类头。
- 在三个医学数据集上分类准确率均超现有方法。
- 适合缺乏大量标注数据的医疗图像场景。
近年来深度学习在医学图像分类与诊断任务中取得显著进展。尽管分类模型在MNIST或ImageNet等简单自然图像数据集上表现稳健，但在数据稀缺且多样性不足的复杂医学图像数据集中，其鲁棒性并不一致。此外，以往研究显示自然图像数据集上存在数据似然与分类精度之间的潜在权衡。本研究探索将基于分数的生成模型用于医学图像分类，特别是乳腺影像。结果表明，所提出的生成分类器在CBIS-DDSM、INbreast和Vin-Dr Mammo数据集上均实现更优的分类性能，并提出了一种更广泛的图像分类新范式。代码已公开于https://github.com/sushmitasarker/sgc_for_medical_image_classification。
原文摘要 · Abstract (English)
The remarkable success of deep learning in recent years has prompted applications in medical image classification and diagnosis tasks. While classification models have demonstrated robustness in classifying simpler datasets like MNIST or natural images such as ImageNet, this resilience is not consistently observed in complex medical image datasets where data is more scarce and lacks diversity. Moreover, previous findings on natural image datasets have indicated a potential trade-off between data likelihood and classification accuracy. In this study, we explore the use of score-based generative models as classifiers for medical images, specifically mammographic images. Our findings suggest that our proposed generative classifier model not only achieves superior classification results on CBIS-DDSM, INbreast and Vin-Dr Mammo datasets, but also introduces a novel approach to image classification in a broader context. Our code is publicly available at https://github.com/sushmitasarker/sgc_for_medical_image_classification
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