Handcrafted Histological Transformer (H2T): A Brief Introduction

Dang Quoc Vu, Kashif Rajpoot, Shan E Ahmed Raza, Nasir Rajpoot

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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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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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