MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
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CFQI extends fitted Q-iteration by using separate modules for compositional task variants to learn policies robust to imbalanced patient sub-populations in medical RL.
Spectra-Scope is a new AutoML framework that trains interpretable machine learning models on spectral data to characterize material properties while enabling users to understand which spectral features drive the predictions.
citing papers explorer
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MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
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Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
CFQI extends fitted Q-iteration by using separate modules for compositional task variants to learn policies robust to imbalanced patient sub-populations in medical RL.
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Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data
Spectra-Scope is a new AutoML framework that trains interpretable machine learning models on spectral data to characterize material properties while enabling users to understand which spectral features drive the predictions.