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WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING

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Citation Author(s):
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai
Submitted by:
Chaoyi Zhang
Last updated:
9 October 2018 - 8:39am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Chaoyi Zhang
Paper Code:
MA.P10.7

Abstract 

Abstract: 

Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis, using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches. In this study, we propose an iterative patch labelling algorithm based on the Convolutional Neural Network (CNN), with a well-designed thresholding scheme, a training policy and a novel discriminative model architecture, to distinguish patches and use the discriminative ones to achieve WSI-classification. Our method is evaluated on the MICCAI 2015 Challenge Dataset, and shows a large improvement over the baseline approaches.

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