Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction
Not on the map. Why?
Abstract (arXiv)
Whole-Slide Image (WSI) is an important tool for estimating cancer prognosis. Current studies generally follow a conventional cancer-specific paradigm in which each cancer corresponds to a single model. However, this paradigm naturally struggles to scale to rare tumors and cannot leverage knowledge from other cancers. While multi-task learning frameworks have been explored recently, they often place high demands on computational resources and require extensive training on ultra-large, multi-cancer WSI datasets. To this end, this paper shifts the paradigm to knowledge transfer and presents the first preliminary yet systematic study on cross-cancer prognosis knowledge transfer in WSIs, called CROPKT. It comprises three major parts. (1) We curate a large dataset (UNI2-h-DSS) with 26 cancers and use it to measure the transferability of WSI-based prognostic knowledge across different cancers (including rare tumors). (2) Beyond a simple evaluation merely for benchmarking, we design a range of experiments to gain deeper insights into the underlying mechanism behind transferability. (3) We further show the utility of cross-cancer knowledge transfer, by proposing a routing-based baseline approach (ROUPKT) that could often efficiently utilize the knowledge transferred from off-the-shelf models of other cancers. CROPKT could serve as an inception that lays the foundation for this nascent paradigm, i.e., WSI-based prognosis prediction with cross-cancer knowledge transfer. Our source code is available at https://github.com/liupei101/CROPKT.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 19 Aug 2025.
Builds on
33 of 52 references.
References · 33
- 2017Attention Is All You Needcited by 60,361
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
- 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
- 2019A Comprehensive Survey on Transfer Learningcited by 1,160
- 2018Attention-based Deep Multiple Instance Learningcited by 1,095
- 2020Meta-Learning in Neural Networks: A Surveycited by 950
- 2017A Survey on Multi-Task Learningcited by 840
- 2018Taskonomy: Disentangling Task Transfer Learningcited by 634
- 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
- 2022ST-MoE: Designing Stable and Transferable Sparse Expert Modelscited by 399
- 2015Return of Frustratingly Easy Domain Adaptationcited by 391
21 more
- 2019Which Tasks Should Be Learned Together in Multi-task Learning?cited by 375
- 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
- 2017Meta Networkscited by 269
- 2021Efficiently Identifying Task Groupings for Multi-Task Learningcited by 222
- 2024Multimodal Whole Slide Foundation Model for Pathologycited by 162
- 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
- 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
- 2017Machine Learning for Survival Analysis: A Surveycited by 112
- 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
- 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 72
- 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
- 2019Deep learning-based survival prediction for multiple cancer types using histopathology imagescited by 47
- 2024Multimodal Prototyping for cancer survival predictioncited by 47
- 2025Do Multiple Instance Learning Models Transfer?cited by 29
- 2022AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Imagescited by 24
- 2023Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classificationcited by 18
- 2023Regression-based Deep-Learning predicts molecular biomarkers from pathology slidescited by 15
- 2023HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Imagecited by 14
- 2024Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathologycited by 14
- 2025Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncodercited by 13
- 2025Distilled Prompt Learning for Incomplete Multimodal Survival Predictioncited by 11
Built on by
Citing papers per year: 2026: 1.
1 paper.