Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
Kalra, et al., Yottixel – an image search engine for large archives of histopathology whole slide images, Medical Image Analysis 65 (2020) 101757
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Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.
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Retrieval-Guided Generation for Safer Histopathology Image Captioning
Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
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Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.
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Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.