Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
Neural radiance fields in medical imaging: A survey
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EchoTrust is an evidence-driven actor-verifier framework that produces structured intermediate representations for more reliable and interpretable reasoning in echocardiography visual language models.
A multi-task model with EfficientNet-B7 predicts COVID-19 and source center using logit-adjusted loss, achieving F1 0.9098 and AUC 0.9647 on 308 multi-center scans.
citing papers explorer
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Weight Group-wise Post-Training Quantization for Medical Foundation Model
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
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Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents
EchoTrust is an evidence-driven actor-verifier framework that produces structured intermediate representations for more reliable and interpretable reasoning in echocardiography visual language models.
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Robust Multi-Source Covid-19 Detection in CT Images
A multi-task model with EfficientNet-B7 predicts COVID-19 and source center using logit-adjusted loss, achieving F1 0.9098 and AUC 0.9647 on 308 multi-center scans.