Name
Abstract | ||
|---|---|---|
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel 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. A few MIL methods extend to multiple scales, but 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 the computational demands with regard to Floating-Point Operations (FLOPs) and processing time by up to 40x. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-19803-8_41 | European Conference on Computer Vision |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Thandiackal | 1 | 0 | 0.34 |
| Boqi Chen | 2 | 0 | 0.34 |
| Pushpak Pati | 3 | 0 | 0.34 |
| Guillaume Jaume | 4 | 1 | 2.75 |
| Drew F. K. Williamson | 5 | 0 | 0.34 |
| Maria Gabrani | 6 | 30 | 7.27 |
| Orçun Göksel | 7 | 31 | 8.92 |