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2021 | OriginalPaper | Chapter

SSLP: Spatial Guided Self-supervised Learning on Pathological Images

Authors : Jiajun Li, Tiancheng Lin, Yi Xu

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Publisher: Springer International Publishing

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Abstract

Nowadays, there is an urgent requirement of self-supervised learning (SSL) on whole slide pathological images (WSIs) to relieve the demand of finely expert annotations. However, the performance of SSL algorithms on WSIs has long lagged behind their supervised counterparts. To close this gap, in this paper, we fully explore the intrinsic characteristics of WSIs and propose SSLP: Spatial Guided Self-supervised Learning on Pathological Images. We argue the patch-wise spatial proximity is a significant characteristic of WSIs, if properly employed, shall provide abundant supervision for free. Specifically, we explore three semantic invariance from 1) self-invariance: the same patch of different augmented views, 2) intra-invariance: the patches within spatial neighbors and 3) inter-invariance: their corresponding neighbors in the feature space. As a result, our SSLP model achieves \(82.9\%\) accuracy and \(85.7\%\) AUC on CAMELYON linear classification and \(95.2\%\) accuracy fine-tuning on cross-disease classification on NCTCRC, which outperforms previous state-of-the-art algorithm and matches the performance of a supervised counterpart.

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Title: SSLP: Spatial Guided Self-supervised Learning on Pathological Images
Authors: Jiajun Li
Tiancheng Lin
Yi Xu
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87195-6

Electronic ISBN: 978-3-030-87196-3

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-87196-3_1

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