@inproceedings{f61d35fb4c4546a19c0462d769b83ff2,
title = "CD-Net: Histopathology Representation Learning Using Context-Detail Transformer Network",
abstract = "Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large fields of view, i.e views providing more contextual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail Modules, which operate on inputs from multiple resolutions. The residual connections between the modules enable the joint training paradigm while learning self-supervised representation for WSIs. The efficacy of CD-Net is demonstrated in classifying Lung Adenocarcinoma from Squamous cell carcinoma (N=1042 WSIs).",
keywords = "context-detail, digital pathology, multi-resolution, self-supervision",
author = "Saarthak Kapse and Srijan Das and Prateek Prasanna",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230626",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
}