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Faculté des Sciences appliquées
Faculté des Sciences appliquées
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Deep learning algorithms applied to dermatological pathology monitoring

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Delarbre, François ULiège
Promotor(s) : Geurts, Pierre ULiège
Date of defense : 9-Sep-2019/10-Sep-2019 • Permalink : http://hdl.handle.net/2268.2/8221
Details
Title : Deep learning algorithms applied to dermatological pathology monitoring
Author : Delarbre, François ULiège
Date of defense  : 9-Sep-2019/10-Sep-2019
Advisor(s) : Geurts, Pierre ULiège
Committee's member(s) : Marée, Raphaël ULiège
Boigelot, Bernard ULiège
Bours, Jérémie 
Language : English
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Professionals of domain
Other
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Nowadays, deep learning techniques are applied in a wide range of problems. With the number of patients and so the numerous data, medical sector is a domain that will benefit from this technology.


In this thesis, we investigate the use of such techniques for dermatology and dermatoscopic applications. More precisely, we apply deep-learning techniques on skin lesion images to monitor nevi and to detect the skin lesions.

To tackle this problem, we first review the literature on deep-learning based solution for semantic segmentation and image retrieval.
We identify the key elements that make a semantic segmentation algorithm performant.
Then, we develop the state of the art algorithms and compare them.
Lastly, we experiment a nevus retrieval algorithm based on our semantic segmentation network.
To train this algorithm, we implement a siamese network and a loss function suitable to this network. we then evaluate the performances of our model.

Based on this analysis, we identify DeepLab as the best model for our use case. We also point out that the image retrieval based on that nevus segmentation algorithm is not efficient.


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Author

  • Delarbre, François ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
  • Boigelot, Bernard ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique
    ORBi View his publications on ORBi
  • Bours, Jérémie
  • Total number of views 55
  • Total number of downloads 0










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