Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, Orcun Goksel
Abstract
"Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requirements and constrains the contextualization of the WSI-level representation to a single scale. Certain MIL methods extend to multiple scales, but they are computationally more demanding. In this paper, inspired by the pathological diagnostic process, we propose ZoomMIL, a method that learns to perform multi-level zooming in an end-to-end manner. ZoomMIL builds WSI representations by aggregating tissue-context information from multiple magnifications. The proposed method outperforms the state-of-the-art MIL methods in WSI classification on two large datasets, while significantly reducing computational demands with regard to Floating-Point Operations (FLOPs) and processing time by 40-50x. Our code is available at: https://github.com/histocartography/zoommil."
BibTeX
@inproceedings{eccv2022_differentiablezo,
title = {Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images},
author = {Kevin Thandiackal and Boqi Chen and Pushpak Pati and Guillaume Jaume and Drew F. K. Williamson and Maria Gabrani and Orcun Goksel},
booktitle = {ECCV 2022},
year = {2022}
}