BenchReAD supplies a systematic retinal anomaly detection benchmark that shows DRA performs best overall yet drops on unseen cases, while NFM-DRA with added normal feature memory reaches new state-of-the-art results.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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WALDO improves zero-shot anomaly localization in medical imaging by selecting reference distributions via entropy-weighted Sliced Wasserstein distances and Goldilocks zone sampling, yielding a 19% relative gain on brain MRI benchmarks.
OASIC uses anomaly-based masking and severity estimation to select occlusion-matched models, improving AUC on occluded images by up to 23.7 points.
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BenchReAD: A systematic benchmark for retinal anomaly detection
BenchReAD supplies a systematic retinal anomaly detection benchmark that shows DRA performs best overall yet drops on unseen cases, while NFM-DRA with added normal feature memory reaches new state-of-the-art results.
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Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
WALDO improves zero-shot anomaly localization in medical imaging by selecting reference distributions via entropy-weighted Sliced Wasserstein distances and Goldilocks zone sampling, yielding a 19% relative gain on brain MRI benchmarks.
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OASIC: Occlusion-Agnostic and Severity-Informed Classification
OASIC uses anomaly-based masking and severity estimation to select occlusion-matched models, improving AUC on occluded images by up to 23.7 points.