TY  - CONF
T1  - Visualizing and interpreting feature reuse of pretrained CNNs for histopathology
A1  - Graziani, Mara
A1  - Andrearczyk, Vincent
A1  - Müller, Henning
TI  - IMVIP 2019
Y1  - 2019
CY  - Dublin, Ireland
N1  - source code: https://git.io/JeqF3
KW  - Deep Learning
KW  - feature reuse
KW  - finetuning
N2  - 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.
ER  -