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Foundation models show modest gains for whole-slide image retrieval in cancer data

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates ten different pipelines for whole-slide image retrieval in cancer pathology data. The research found that while the TITAN foundation model performed best, its advantage over patch-based and supervised methods was minimal. Performance varied significantly by organ and diagnosis, with challenging subtypes showing low accuracy, indicating limitations in morphology-based retrieval for clinical deployment. AI

IMPACT Highlights limitations in current morphology-based AI for cancer diagnosis, suggesting multimodal approaches are needed for clinical deployment.

RANK_REASON Academic paper evaluating foundation models for image retrieval in a medical context. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Foundation models show modest gains for whole-slide image retrieval in cancer data

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Academic paper evaluating foundation models for image retrieval in a medical context. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly, Wataru Uegami, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh ·

    Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data

    arXiv:2605.00902v1 Announce Type: new Abstract: Foundation models are reshaping computational histopathology, yet their value for whole-slide image retrieval relative to strong patch-based and supervised aggregation baselines remains unclear. We benchmarked ten pipelines on 9,387…

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