The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
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LAION-5B is an openly released dataset of 5.85 billion CLIP-filtered image-text pairs that enables replication of foundational vision-language models.
The α-index is a conserved position-weighted authorship framework with a senior-author penalty that decreases credit as the number of middle authors increases.
A quantum prototype learning scheme encodes class representatives as generative matrix product states and performs classification and clustering via geometric measures in Hilbert space, outperforming classical prototypes on Fashion-MNIST and ECG data.
A dual-edge graph fuses vessel-lesion geometry and embedding-biomarker sensitivity from four aligned streams to produce interpretable DR grades on APTOS images with 0.8076 accuracy.
Empirical benchmark finds attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) achieve highest AUC above 84% on RFMiD binary screening and best F1 scores on multi-label task, with VLMs competitive but not superior and external Messidor-2 AUC 66.8-84.7%.
citing papers explorer
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Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification
The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
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LAION-5B: An open large-scale dataset for training next generation image-text models
LAION-5B is an openly released dataset of 5.85 billion CLIP-filtered image-text pairs that enables replication of foundational vision-language models.
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The $\alpha$-Index: A Penalized Authorship-Integrity Framework for Position-Weighted Scientific Contribution
The α-index is a conserved position-weighted authorship framework with a senior-author penalty that decreases credit as the number of middle authors increases.
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Geometric Prototype Learning in Quantum Hilbert Space with Matrix Product States
A quantum prototype learning scheme encodes class representatives as generative matrix product states and performs classification and clustering via geometric measures in Hilbert space, outperforming classical prototypes on Fashion-MNIST and ECG data.
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A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading
A dual-edge graph fuses vessel-lesion geometry and embedding-biomarker sensitivity from four aligned streams to produce interpretable DR grades on APTOS images with 0.8076 accuracy.
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Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
Empirical benchmark finds attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) achieve highest AUC above 84% on RFMiD binary screening and best F1 scores on multi-label task, with VLMs competitive but not superior and external Messidor-2 AUC 66.8-84.7%.