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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Deep interactive learning for digital pathology

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Le, Ba ULiège
Promotor(s) : Marée, Raphaël ULiège
Date of defense : 24-Jun-2021/25-Jun-2021 • Permalink : http://hdl.handle.net/2268.2/11470
Details
Title : Deep interactive learning for digital pathology
Translated title : [fr] Apprentissage interactif approfondi pour la pathologie numérique
Author : Le, Ba ULiège
Date of defense  : 24-Jun-2021/25-Jun-2021
Advisor(s) : Marée, Raphaël ULiège
Committee's member(s) : Van Droogenbroeck, Marc ULiège
Louveaux, Quentin ULiège
Language : English
Number of pages : 94
Keywords : [en] Cytomine
[en] Pathology
[en] Deep Learning
[en] Computer Vision
[en] Image Segmentation
Discipline(s) : Engineering, computing & technology > Computer science
Research unit : Cytomine ULiège Research & Development
Target public : Researchers
Professionals of domain
Student
General public
Complementary URL : https://github.com/bathienle/master-thesis-code
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en sciences informatiques, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] In many biomedical applications, manual annotations of whole slide images take a tremendous amount of time. In the computer vision literature, semi-automatic tools using deep learning, known as deep interactive learning, have emerged to speed up the annotation process. These semi-automatic tools exploit the interactions of the annotators in various forms to produce the annotations more rapidly. In recent years, deep interactive learning seems to gain more attention for its performance. However, do the additional information provided by the annotators help to improve the results of automatic tools? An exploration in the literature was made, resulting in the finding of a promising architecture, named NuClick, which uses the scribbles of the annotators in combination with the images to produce decent annotations more quickly. In this thesis, results of the conducted experiments on various datasets show that the additional information provided by the scribbles improve drastically the performance of the segmentation for tissues, such as bronchi, glands, or infiltrations. However, this interactive approach fails to produce accurate segmentation for more complex tissues, such as tumours or inflammations. Also, results indicate that the quality of the scribbles highly influences the produced segmentation. Therefore, care should be taken when the annotators scribble the objects of interest. These results tend to support the benefit that can be gain from the interactions of the annotators, although this thesis shows that there is room for improvements with these semi-automatic tools.


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Author

  • Le, Ba ULiège Université de Liège > Master sc. informatiques, à fin.

Promotor(s)

Committee's member(s)

  • Van Droogenbroeck, Marc ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
    ORBi View his publications on ORBi
  • Louveaux, Quentin ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
    ORBi View his publications on ORBi
  • Total number of views 173
  • Total number of downloads 114










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