Zhang, Hongrun, Meng, Yanda 
ORCID: 0000-0001-7344-2174, Zhao, Yitian, Qiao, Yihong, Yang, Xiaoyun, Coupland, Sarah E ORCID: 0000-0002-1464-2069 and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2022)
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning
for Histopathology Whole Slide Image Classification.
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2203.12081v1.pdf - Author Accepted Manuscript Download (9MB) | Preview |
Abstract
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classification problem still face unique challenges, particularly those related to small sample cohorts. In these, there are limited number of WSI slides (bags), while the resolution of a single WSI is huge, which leads to a large number of patches (instances) cropped from this slide. To address this issue, we propose to virtually enlarge the number of bags by introducing the concept of pseudo-bags, on which a double-tier MIL framework is built to effectively use the intrinsic features. Besides, we also contribute to deriving the instance probability under the framework of attention-based MIL, and utilize the derivation to help construct and analyze the proposed framework. The proposed method outperforms other latest methods on the CAMELYON-16 by substantially large margins, and is also better in performance on the TCGA lung cancer dataset. The proposed framework is ready to be extended for wider MIL applications. The code is available at: https://github.com/hrzhang1123/DTFD-MIL
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Additional Information: | Accepted to CVPR2022 |
| Uncontrolled Keywords: | cs.CV, cs.CV, cs.AI, cs.LG |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 25 Apr 2022 13:24 |
| Last Modified: | 18 Jan 2023 21:04 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3153762 |
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