Visualizing and interpreting feature reuse of pretrained CNNs for histopathology

Graziani, Mara (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); University of Geneva, Switzerland) ; Andrearczyk, Vincent (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); University of Geneva, Switzerland)

Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel activations of deep layers in the pretrained network are not present after finetuning. The learned features seem to be used by the network to spot atypical nuclei in the images, as shown by class activation maps. Finally, texture measures appear discriminative after finetuning, as shown by accurate Regression Concept Vectors.


Keywords:
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Dublin, Ireland, Irish Pattern Recognition and Classification Society
Date:
2019-08
Dublin, Ireland
Irish Pattern Recognition and Classification Society
Pagination:
4 p.
Published in:
MVIP 2019: Irish Machine Vision and Image Processing Conference Proceedings
Author of the book:
Courtney, Jane ; ed.; Technological University Dublin
Deegan, Catherine ; ed.; Technological University Dublin
Leamy, Paul ; ed.; Technological University Dublin
ISBN:
9780993420740
External resources:
Appears in Collection:



 Record created 2019-10-25, last modified 2019-11-28

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