用于数字病理图像解耦的半监督对比变分自编码器
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
AI总结：
本文提出首个面向病理图像的解耦方法，通过级联解耦、新架构和重建分支提升肿瘤浸润淋巴细胞检测模型的可解释性与泛化能力。
摘要：
尽管深度学习模型具有强大的预测能力，其可解释性仍然是一个重要关切。解耦模型通过将潜在空间分解为可解释的子空间来提升可解释性。本文提出了首个面向病理图像的解耦方法。我们聚焦于检测肿瘤浸润淋巴细胞（TIL）的任务。我们提出了多种思路，包括级联解耦、新颖的架构以及重建分支。我们在复杂的病理图像上取得了优越的性能，从而提升了TIL检测深度学习模型的可解释性乃至泛化能力。我们的代码可在 https://github.com/Shauqi/SS-cVAE 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
