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Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images. (arXiv:2202.07001v2 [eess.IV] UPDATED)
Sept. 8, 2022, 1:14 a.m. | Quoc Dang Vu, Kashif Rajpoot, Shan E Ahmed Raza, Nasir Rajpoot
cs.CV updates on arXiv.org arxiv.org
Diagnostic, prognostic and therapeutic decision-making of cancer in pathology
clinics can now be carried out based on analysis of multi-gigapixel tissue
images, also known as whole-slide images (WSIs). Recently, deep convolutional
neural networks (CNNs) 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 …
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