@inproceedings{cbe1a0aa425a454394bc3b547fb2e284,
title = "PMIL: A Topology Module to Improve MIL-based WSI Classification",
abstract = "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.",
keywords = "Histopathology, Multiple Instance Learning, Persistence Homology, Topological Data Analysis",
author = "Ahmad Obeid and Anabia Sohail and Said Boumaraf and Xiabi Liu and Sajid Javed and Hasan Almarzouqi and Jorge Dias and Mohammed Bennamoun and Naoufel Werghi and Ibrahim Elfadel",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025 ; Conference date: 25-05-2025 Through 28-05-2025",
year = "2025",
doi = "10.1109/ISCAS56072.2025.11043738",
language = "British English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings",
address = "United States",
}