Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Open accessAbstract
Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task. Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level. Attention-based aggregation is more flexible and adaptive than aggregation techniques in recent survival models. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions. The proposed framework can also be used to assess individual patient's risk and thus assisting in delivering personalized medicine.
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The FSM model that leveraged WSI and genomic data shows a marked improvement in performance across both metrics (c-index & tAUC) and datasets (TCGA & CPTAC), outperforming other methods such as Attention MIL [21], Patch-GCN [25], DeepAttnMISL [27], PathomicFusion [28], PORPOISE [5] and MOTCAT [29].
“The FSM model that leveraged WSI and genomic data shows a marked improvement in performance across both metrics (c-index & tAUC) and datasets (TCGA & CPTAC), outperforming other methods such as Attention MIL [21], Patch-GCN [25], DeepAttnMISL [27], PathomicFusion [28], PORPOISE [5] and MOTCAT [29].”
From Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival (2024) · cites this paper as [27]
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- Authors
- Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., & Huang, J.
- Affiliations
- The University of Texas at Arlington (US)
- UNSW Sydney (AU)
- Corresponding author
- Huang, Junzhou
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 65
- Pages
- 101789
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W3043535018
- Cited by
- 522
- References
- 68
- Citations by year
- 2026: 97 · 2025: 116 · 2024: 102 · 2023: 94 · 2022: 77 · 2021: 32 · 2020: 4
- FWCI
- 20.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- whole slide images, cancer survival prediction, multiple instance learning, histopathological images, interpretability, personalized medicine, risk assessment
- MeSH terms
- Humans, Neoplasms
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