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Foundation models show promise in cancer prediction but face generalization challenges

By PulseAugur Editorial · [2 sources] ·

Researchers are exploring the use of foundation models for predicting head and neck cancer recurrence, comparing their performance against traditional radiomics and deep learning methods. One study found that a foundation model derived from CT images outperformed radiomics and deep learning models in predicting distant metastasis risk, achieving an AUC of 0.791. However, another investigation highlighted challenges in generalizing these foundation models across diverse clinical settings and imaging distributions, suggesting that integrating imaging features with clinical data remains the most accurate approach for prognostic prediction. AI

IMPACT Foundation models show potential in medical diagnostics, but further research is needed to ensure their reliability across diverse clinical data.

RANK_REASON The cluster contains two research papers investigating the application of foundation models in medical imaging for cancer prediction.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Foundation models show promise in cancer prediction but face generalization challenges

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The cluster contains two research papers investigating the application of foundation models in medical imaging for cancer prediction.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer

    Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, whi…

  2. arXiv cs.CV TIER_1 English(EN) · Bilel Guetarni, Feryal Windal, David Pasquier, Halim Benhabiles ·

    Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Prediction

    arXiv:2608.00071v1 Announce Type: new Abstract: The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to traditional radiomics which is known to suffer from reproducibility issues and sensi…

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