Attention to detail: inter-resolution knowledge distillation
Rocío del Amor Julio Silva-Rodríguez Adrián Colomer Valery Naranjo
The development of computer vision solutions for gigapixel images in digital
pathology is hampered by significant computational limitations due to the large
size of whole slide images. In particular, digitizing biopsies at high
resolutions is a time-consuming process, which is necessary due to the
worsening results from the decrease in image detail. To alleviate this issue,
recent literature has proposed using knowledge distillation to enhance the
model performance at reduced image resolutions. In particular, soft labels and
features extracted at the highest magnification level are distilled into a
model that takes lower-magnification images as input. However, this approach
fails to transfer knowledge about the most discriminative image regions in the
classification process, which may be lost when the resolution is decreased. In
this work, we propose to distill this information by incorporating attention
maps during training. In particular, our formulation leverages saliency maps of
the target class via grad-CAMs, which guides the lower-resolution Student model
to match the Teacher distribution by minimizing the l2 distance between them.
Comprehensive experiments on prostate histology image grading demonstrate that
the proposed approach substantially improves the model performance across
different image resolutions compared to previous literature.