Hybrid quantum-classical corrective diffusion model improves MAE and CRPS on 2020 validation wind data but exhibits a generalization gap on 2021 out-of-distribution tests.
U-net: Convolutional networks for biomedical image segmentation,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.
citing papers explorer
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Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling
Hybrid quantum-classical corrective diffusion model improves MAE and CRPS on 2020 validation wind data but exhibits a generalization gap on 2021 out-of-distribution tests.
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Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis
Rad-VLSM is a cross-modal two-stage framework that converts semantic guidance from BLIP-2 into box prompts for SAM-based lesion segmentation and then uses the resulting masks as spatial priors in a visual-radiomics fusion head for diagnosis.
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Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.