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Negative Pseudo Labeling Using Class Proportion for Semantic Segmentation in Pathology

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360)

Abstract

In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for pathological images has been recorded as diagnostic information in some hospitals. In this paper, we propose a subtype segmentation method that uses such proportional labels as weakly supervised labels. If the estimated class rate is higher than that of the annotated class rate, we generate negative pseudo labels, which indicate, “input image does not belong to this negative label,” in addition to standard pseudo labels. It can force out the low confidence samples and mitigate the problem of positive pseudo label learning which cannot label low confident unlabeled samples. Our method outperformed the state-of-the-art semi-supervised learning (SSL) methods.

Keywords

Pathological image Semantic segmentation Negative learning Semi-supervised learning Learning from label proportion 

Notes

Acknowledgment

This work was supported by JSPS KAKENHI Grant Number 20H04211.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.Kyoto University HospitalKyotoJapan
  3. 3.Research Center for Medical BigdataNational Institute of InformaticsTokyoJapan

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