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Molecular-driven Foundation Model for Oncologic Pathology

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arxiv 2501.16652 v1 pith:FPMX3KNK submitted 2025-01-28 cs.CV cs.AI

Anurag Vaidya , Andrew Zhang , Guillaume Jaume , Andrew H. Song , Tong Ding , Sophia J. Wagner , Ming Y. Lu , Paul Doucet
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Harry Robertson Cristina Almagro-Perez Richard J. Chen Dina ElHarouni Georges Ayoub Connor Bossi Keith L. Ligon Georg Gerber Long Phi Le Faisal Mahmood
This is my paper · ORCID
classification cs.CVcs.AI
keywords foundationmodelthreadsmodelspredictiontasksclinicaldownstream
verification ladder T0 review T1 audit T2 compute T3 formal
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Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles - the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables Threads to capture the tissue's underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction, and survival prediction, Threads outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Do Multiple Instance Learning Models Transfer?

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Pretrained multiple instance learning models transfer across organs and tasks in computational pathology, and pancancer pretraining can rival slide foundation models with far less data.

  2. MOOZY: A Patient-First Foundation Model for Computational Pathology

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Patient-level pretraining with a case transformer and multi-task public supervision yields transferable WSI embeddings that beat larger slide-centric models on held-out pathology tasks.

  3. Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Multimodal fusion of CT and pathology images improves recurrence risk prediction in kidney cancer, with the best model approaching the clinical Leibovich score.

  4. A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics

    q-bio.GN 2025-08 unverdicted novelty 6.0 of 10

    HESCAPE shows that cross-modal contrastive pretraining helps mutation classification but hurts gene expression prediction in spatial transcriptomics, implicating batch effects.

  5. Towards Robust Foundation Models for Digital Pathology

    eess.IV 2025-07 conditional novelty 6.0 of 10

    PathoROB shows that all 20 evaluated pathology foundation models encode medical center information and that lower robustness correlates with larger downstream performance drops.

  6. SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SurgVLM, a family of surgical vision-language models trained on 1.81M frames and 7.79M conversations, outperforms 14 commercial VLMs on a six-dataset surgical benchmark.

  7. The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A label-free attack that perturbs only 0.1% of patches in a whole-slide image can shift the model's global representation and substantially degrade downstream pathology task accuracy.

  8. PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology

    cs.CV 2025-05 reject novelty 6.0 of 10

    A large private multi-center benchmark of 19 pathology foundation models on 64 tasks finds Virchow2 and H-Optimus-1 best overall, with vision-language models lagging behind.

  9. Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    FG-PAN improves zero-shot brain tumor subtype classification by aligning refined visual patch features with LLM-generated fine-grained text prototypes.

  10. Emerging AI Approaches for Cancer Spatial Omics

    q-bio.QM 2025-06 unverdicted novelty 2.0 of 10

    A review that groups AI methods for cancer spatial omics into data-driven, constraint-based, and mechanistic modeling paradigms, calling for more interpretable models and mouse-model-generated perturbational data.

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