@inproceedings{95beadd3ab99496aa92799675ccc999a,
title = "Semi-supervised Contrastive VAE for Disentanglement of Digital Pathology Images",
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 https://github.com/Shauqi/SS-cVAE.",
keywords = "Contrastive VAE, Digital Pathology, Disentanglement",
author = "Mahmudul Hasan and Xiaoling Hu and Shahira Abousamra and Prateek Prasanna and Joel Saltz and Chao Chen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1007/978-3-031-72083-3\_43",
language = "English",
isbn = "9783031720826",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "459--469",
editor = "Linguraru, \{Marius George\} and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Schnabel, \{Julia A.\}",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings",
}