Comparison of deep transfer learning strategies for digital pathology

Mormont, Romain; Geurts, Pierre; Marée, Raphaël

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Comparison of deep transfer learning strategies for digital pathology

Mormont, Romain; Geurts, Pierre; Marée, Raphaël

2018 • In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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https://hdl.handle.net/2268/222511

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Keywords :

deep learning; transfer learning; digital pathology; object recognition

Abstract :

[en] In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

Research Center/Unit :

Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège

Disciplines :

Computer science

Author, co-author :

Mormont, Romain ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique

Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique

Marée, Raphaël ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Language :

English

Title :

Comparison of deep transfer learning strategies for digital pathology

Alternative titles :

[fr] Comparaison de stratégies de transfert profond pour la pathologie digitale

Publication date :

2018

Event name :

3rd Computer Vision for Microscopy Image Analysis (CVMI) workshop at the 2018 IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR)

Event organizer :

IEEE Computer Society, Computer Vision Foundation

Event place :

Salt Lake City, United States

Event date :

du 18 juin 2018 au 22 juin 2018

Audience :

International

Main work title :

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Publisher :

IEEE

Peer reviewed :

Peer reviewed

Available on ORBi :

since 21 April 2018

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