DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis

• URL: http://arxiv.org/abs/2109.05788v1
• Date: Mon, 13 Sep 2021 09:10:43 GMT
• Title: DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis
• Authors: Tiange Xiang, Yang Song, Chaoyi Zhang, Dongnan Liu, Mei Chen, Fan Zhang, Heng Huang, Lauren O'Donnell, Weidong Cai
• Abstract summary: We present a novel weakly-supervised framework for classifying whole slide images (WSIs) WSIs are commonly processed by patch-wise classification with patch-level labels. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label.
• Score: 78.78181964748144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classification with patch-level labels. However, patch-level labels require precise annotations, which is expensive and usually unavailable on clinical data. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label. To address this issue, we posit that WSI analysis can be effectively conducted by integrating information at both high magnification (local) and low magnification (regional) levels. We auto-encode the visual signals in each patch into a latent embedding vector representing local information, and down-sample the raw WSI to hardware-acceptable thumbnails representing regional information. The WSI label is then predicted with a Dual-Stream Network (DSNet), which takes the transformed local patch embeddings and multi-scale thumbnail images as inputs and can be trained by the image-level label only. Experiments conducted on two large-scale public datasets demonstrate that our method outperforms all recent state-of-the-art weakly-supervised WSI classification methods.

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