Abstract
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 this https URL.
Abstract (translated)
尽管深度学习模型的预测能力很强，但它们的可解释性仍然是一个重要的问题。解离模型通过将潜在空间分解为可解释子空间来增加可解释性。在本文中，我们提出了第一个用于病理图像的解离方法。我们专注于肿瘤浸润淋巴细胞（TIL）的检测任务。我们提出了包括级联解离、新架构和重构支路等不同想法。我们在复杂病理图像上的表现优于其他深度学习模型，从而提高了TIL检测深度学习模型的可解释性和泛化能力。我们的代码可在此处访问：https://www.xxx.com/
URL
https://arxiv.org/abs/2410.02012
