A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
Medical image analysis42, 60–88 (2017)
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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.
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Bridging visual saliency and large language models for explainable deep learning in medical imaging
A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
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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.