Relieving pixel-wise labeling effort for pathology image segmentation with self-training

[en] Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house dataset, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain.

Disciplines :

Computer science

Author, co-author :

Mormont, Romain; University of Liège

TESTOURI, Mehdi ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN

Marée, Raphaël; University of Liège

Geurts, Pierre; University of Liège

External co-authors :

yes

Language :

English

Title :

Relieving pixel-wise labeling effort for pathology image segmentation with self-training

Publication date :

October 2022

Main work title :

Lecture Notes in Computer Science

Publisher :

Springer, Cham

ISBN/EAN :

978-3-031-25082-8

Peer reviewed :

Peer reviewed

Available on ORBilu :

since 17 September 2023

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15 (1 by Unilu)

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Scopus citations^®
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