PMIL: A Topology Module to Improve MIL-based WSI Classification
Deep learning models have achieved remarkable success in pathology image analysis. However, they still face challenges in effectively modeling fine-grained, object-level features. Topological Data Analysis (TDA) has shown promise for addressing these issues but remains underexplored, particularly for whole-slide pathology applications. Additionally, the effectiveness of TDA has yet to be firmly established, as current studies largely use small-scale datasets. In this work, we address these gaps by introducing Persistent Homology in Multiple Instance Learning (PMIL), the first adaptable TDA-based module within the MIL framework. We validate our approach on a large-scale classification dataset, benchmarking against multiple state-of-the-art methods.
Authors
- Ahmad Obeid (ORCID: https://orcid.org/0000-0002-8297-5834)
- Said Boumaraf (ORCID: https://orcid.org/0000-0001-8154-7195)
- Mohammed Bennamoun (ORCID: https://orcid.org/0000-0002-6603-3257)
- Jorge Dias (ORCID: https://orcid.org/0000-0002-2725-8867)
- Anabia Sohail (ORCID: https://orcid.org/0000-0003-2961-6098)
- Hasan Al-Marzouqi (ORCID: https://orcid.org/0000-0002-2826-1515)
- Ibrahim M. Elfadel (ORCID: https://orcid.org/0000-0003-3220-9987)
- Naoufel Werghi (ORCID: https://orcid.org/0000-0002-5542-448X)
- Sajid Javed (ORCID: https://orcid.org/0000-0002-0036-2875)
- Xiabi Liu (ORCID: https://orcid.org/0000-0003-1633-0648)
Institutions
- Beijing Institute of Technology (CN)
- Khalifa University of Science and Technology (AE)
- The University of Western Australia (AU)
Publication Details
- Published
- 2025-05-25
- DOI
- https://doi.org/10.1109/iscas56072.2025.11043738
- Citations
- 1
- Primary Topic
- Industrial Technology and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Terry Fox Foundation