Handcrafted Histological Transformer (H2T): A Brief IntroductionDownload PDF

22 Apr 2022, 20:24 (modified: 04 Jun 2022, 12:07)MIDL 2022 Short PapersReaders: Everyone
Keywords: Computational Pathology, Unsupervised Learning, Deep Learning, WSI Representation, Transformer
Abstract: Recently, deep neural networks (DNNs) have been proposed to derive unsupervised WSI representations; these are attractive as they rely less on expert annotation which is cumbersome. However, a major trade-off is that higher predictive power generally comes at the cost of interpretability, posing a challenge to their clinical use where transparency in decision-making is generally expected. To address this challenge, we present a handcrafted framework based on DNN for constructing holistic WSI-level representations.
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Paper Type: recently published or submitted journal contributions
Primary Subject Area: Unsupervised Learning and Representation Learning
Secondary Subject Area: Application: Histopathology
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