Faculté des Sciences appliquées
Faculté des Sciences appliquées

Deep Learning for Content-Based Image Retrieval in Biomedical applications

Schyns, Axelle ULiège
Promotor(s) : Marée, Raphaël ULiège ; Geurts, Pierre ULiège
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink :
Title : Deep Learning for Content-Based Image Retrieval in Biomedical applications
Translated title : [fr] Apprentissage profond pour la recherche d'images basée sur le contenu dans les applications biomédicales
Author : Schyns, Axelle ULiège
Date of defense  : 26-Jun-2023/27-Jun-2023
Advisor(s) : Marée, Raphaël ULiège
Geurts, Pierre ULiège
Committee's member(s) : Louppe, Gilles ULiège
Language : English
Number of pages : 101
Keywords : [en] Deep Learning
[en] CBIR
[en] histopathology
[en] self-supervised
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
Professionals of domain
Complementary URL :
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] Due to advances in the digital field, the number of images being generated every day grows exponentially. The field of histopathology is no exception and witnesses the emergence of an increasing number of Whole Slide Images that need to be treated, analyzed and diagnosed. One way to facilitate the diagnostic process is by comparing a particular case with other similar cases. This implies, first, the accessibility to other cases, as well as the ability to retrieve the most useful ones, i.e., the most similar cases. To achieve the latter goal, the technique of Content-Based Image Retrieval (CBIR) was conceived. CBIR involves retrieving the most similar images in a database to a given query image.

The goal of this thesis is to study the different elements that compose a CBIR framework and the options available for them, with a specific focus on the feature extraction part of the framework. It offers an open-source implementation that allows the combination of the researched options to create a fully operational CBIR framework. It provides both supervised and self-supervised models as a way to accommodate all situations and datasets.

All feature extraction models are trained on a single dataset containing over 600,000 histopathological images and evaluated on approximately 200,000 different images from the same dataset. Extensive experiments are conducted to analyze the resilience of the frameworks in different situations, such as when dealing with new data or handling class imbalance.

While the supervised models have displayed great results and the self-supervised methods have demonstrated great potential, the scope of what could have been achieved is limited by the lack of evaluation by trained pathologists and by the few remaining untested combinations.




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  • Schyns, Axelle ULiège Université de Liège > Master ingé. civ. info., à fin.


Committee's member(s)

  • Louppe, Gilles ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
    ORBi View his publications on ORBi
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  • Total number of downloads 90

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