Name
Title | ||
|---|---|---|
Cross-Scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images. |
Abstract | ||
|---|---|---|
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-18814-5_3 | International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) |
Keywords | DocType | Volume |
Attention Mechanism,Multi-instance Learning,Multi-scale,Pathology | Conference | 13594 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
15 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruining Deng | 1 | 5 | 3.23 |
| Can Cui | 2 | 0 | 0.34 |
| Lucas W Remedios | 3 | 0 | 0.34 |
| Shunxing Bao | 4 | 12 | 3.38 |
| R Michael Womick | 5 | 0 | 0.34 |
| Sophie Chiron | 6 | 0 | 0.34 |
| Jia Li | 7 | 0 | 0.34 |
| Joseph T. Roland | 8 | 2 | 1.13 |
| Ken S Lau | 9 | 0 | 1.01 |
| Qi Liu | 10 | 568 | 49.57 |
| Keith T Wilson | 11 | 0 | 0.34 |
| Yaohong Wang | 12 | 0 | 0.34 |
| Lori A Coburn | 13 | 0 | 0.34 |
| Bennett A Landman | 14 | 0 | 0.34 |
| Yuankai Huo | 15 | 0 | 0.34 |