用统一模型同时实现可解释分类与医学图像生成
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
- 共享参数的扩散模型联合学习分类与生成任务
- 在少量标注数据下性能接近先进方法，且能生成反事实解释
- 适合需要高可信度与可解释性的医疗AI应用
我们提出Mediffusion——一种基于联合扩散模型的可解释半监督学习方法。医学影像领域因标注数据稀缺而面临挑战：既难以支撑标准训练，又要求模型具备高性能、高置信度与可解释性。本文通过单一共享参数化模型，将分类任务与基于扩散的生成任务相结合，使模型在利用有标签和无标签数据的同时，可通过反事实样本提供准确解释。实验表明，Mediffusion在性能上可媲美近期先进半监督方法，且解释更可靠、更精确。
原文摘要 · Abstract (English)
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of the models. In this work, we propose to tackle those challenges with a single model that combines standard classification with a diffusion-based generative task in a single shared parametrisation. By sharing representations, our model effectively learns from both labeled and unlabeled data while at the same time providing accurate explanations through counterfactual examples. In our experiments, we show that our Mediffusion achieves results comparable to recent semi-supervised methods while providing more reliable and precise explanations.
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