@inproceedings{e4b581388082458d8d5d5429f1775f38,
title = "Patient-Level Microsatellite Stability Assessment from Whole Slide Images by Combining Momentum Contrast Learning and Group Patch Embeddings",
abstract = "Assessing microsatellite stability status of a patient{\textquoteright}s colorectal cancer is crucial in personalizing treatment regime. Recently, convolutional-neural-networks (CNN) combined with transfer-learning approaches were proposed to circumvent traditional laboratory testing for determining microsatellite status from hematoxylin and eosin stained biopsy whole slide images (WSI). However, the high resolution of WSI practically prevent direct classification of the entire WSI. Current approaches bypass the WSI high resolution by first classifying small patches extracted from the WSI, and then aggregating patch-level classification logits to deduce the patient-level status. Such approaches limit the capacity to capture important information which resides at the high resolution WSI data. We introduce an effective approach to leverage WSI high resolution information by momentum contrastive learning of patch embeddings along with training a patient-level classifier on groups of those embeddings. Our approach achieves up to 7.4% better accuracy compared to the straightforward patch-level classification and patient level aggregation approach with a higher stability (AUC, 0.91 ± 0.01 vs. 0.85 ± 0.04, p-value < 0.01). Our code can be found at https://github.com/TechnionComputationalMRILab/colorectal_cancer_ai.",
keywords = "Colorectal cancer, Digital pathology, Momentum contrast learning, Self-supervised learning",
author = "Daniel Shats and Hadar Hezi and Guy Shani and Maruvka, {Yosef E.} and Moti Freiman",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th European Conference on Computer Vision, ECCV 2022 ; Conference date: 23-10-2022 Through 27-10-2022",
year = "2023",
doi = "https://doi.org/10.1007/978-3-031-25066-8_25",
language = "الإنجليزيّة",
isbn = "9783031250651",
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 = "454--465",
editor = "Leonid Karlinsky and Tomer Michaeli and Ko Nishino",
booktitle = "Computer Vision – ECCV 2022 Workshops, Proceedings",
address = "ألمانيا",
}