bibtext: @InProceedings{ Has_SemiSupervised_MICCAI2024,
                   author = { Hasan, Mahmudul and Hu, Xiaoling and Abousamra, Shahira and Prasanna, Prateek and Saltz, Joel and Chen, Chao },
                   title = { { Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
authors: Hasan, Mahmudul Hu, Xiaoling Abousamra, Shahira Prasanna, Prateek Saltz, Joel Chen, Chao