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 datasets, 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 :

Electrical & electronics engineering

Author, co-author :

Mormont, Romain ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Testouri, Mehdi; Unilu - Université du Luxembourg [LU] > Interdisciplinary Centre for Security, Reliability and Trust > Services and Data Management (SEDAN)

Marée, Raphaël ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Geurts, Pierre ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Language :

English

Title :

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

Alternative titles :

[fr] Utilisation de l'auto-apprentissage pour réduire le coût d'annotation pour la segmentation d'image en pathology digitale

Publication date :

October 2022

Event name :

European Conference in Compter Vision (ECCV2022)

Event organizer :

European Computer Vision Association

Event place :

Tel Aviv, Israel

Event date :

October 23, 2022

Audience :

International

Main work title :

Lecture Notes in Computer Science

Publisher :

Springer Cham, Genève, Switzerland

Peer reviewed :

Peer reviewed

Available on ORBi :

since 17 August 2022

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