提出首个病理图像解耦方法，提升肿瘤浸润淋巴细胞检测的可解释性。
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
- 通过级联解耦与新架构设计，分离病理图像潜在特征。
- 在复杂病理图像上实现更优的肿瘤浸润淋巴细胞检测性能。
- 适合关注医学图像可解释性与模型泛化能力的研究者。
尽管深度学习模型具备强大的预测能力，其可解释性仍是重要挑战。解耦模型通过将潜在空间分解为可解释子空间来增强可解释性。本文首次提出针对病理图像的解耦方法，聚焦于肿瘤浸润淋巴细胞（TIL）检测任务。我们引入级联解耦、新型网络架构及重建分支等创新思路，在复杂病理图像上取得优异性能，显著提升了TIL检测模型的可解释性与泛化能力。代码已开源：https://github.com/Shauqi/SS-cVAE。
原文摘要 · Abstract (English)
Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing the latent space into interpretable subspaces. In this paper, we propose the first disentanglement method for pathology images. We focus on the task of detecting tumor-infiltrating lymphocytes (TIL). We propose different ideas including cascading disentanglement, novel architecture, and reconstruction branches. We achieve superior performance on complex pathology images, thus improving the interpretability and even generalization power of TIL detection deep learning models. Our codes are available at https://github.com/Shauqi/SS-cVAE.
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