Whole Slide Image classification via iterative patch labelling

Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationproceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781479970612
Publication statusPublished - Oct 2018
Externally publishedYes
Event25th IEEE International Conference on Image Processing, ICIP 2018: Imaging beyond imagination - Athens, Greece
Duration: 7 Oct 201810 Oct 2018


Conference25th IEEE International Conference on Image Processing, ICIP 2018


  • Iterative patch labelling
  • brain cancer
  • WSI
  • discriminative patches
  • classification


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