[IEEE TMI 2024] MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
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Updated
Oct 18, 2025 - Python
[IEEE TMI 2024] MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
A list of awesome resources related to constraint learning
[CVPRW2024 FGVC11 (Best paper award)] Official pytorch implementation of the paper: "ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery"
Interpretable spatial graph framework integrating pathway and ligand–receptor priors with tissue architecture. Generates pathway maps and H&E overlays that reveal how tumors organize and rewire signaling in space.
Interpretable multimodal neural network framework that integrates single-cell and spatial omics through biologically constrained, concept-bottleneck architectures.
Interpretable perturb-seq modeling with a pathway/TF concept bottleneck — predicts effects and identifies stable regulatory drivers that generalize across datasets.
Interpretable drug-response GNN with a pathway/TF concept-bottleneck; built for cross-panel generalization and conserved-subnetwork discovery.
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