A training-free prototype memory-guided framework for multi-class prenatal ultrasound anomaly classification and localization using few reference images per class, validated on a 9-category multi-center dataset.
In: arxiv:2509.06467 (2026)
6 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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representative citing papers
MaRS improves reconstruction-based OOD detection by replacing L2 residual norms with variance-aware Mahalanobis scoring on autoencoder outputs.
Frozen ViT embeddings in chest radiography suppress small-lesion signal at the CLS token but recover it via patch-local pooling on the same forward pass across multiple models and large cohorts.
ORACLE-CT improves CT classification performance by using anatomy-specific support pooling based on multi-organ segmentation, showing gains in AUROC on internal and external datasets.
DINOv3 at 512x512 resolution with ConvNeXt-B outperforms prior initializations for adult chest X-ray classification but shows no benefit in pediatric cohorts or at 1024 resolution.
Large-scale real-world study demonstrates that bottlenecking pre-trained ViT encoders to a scalar per patch produces interpretable affordance features useful for navigation policies.
citing papers explorer
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Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
A training-free prototype memory-guided framework for multi-class prenatal ultrasound anomaly classification and localization using few reference images per class, validated on a 9-category multi-center dataset.
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MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring
MaRS improves reconstruction-based OOD detection by replacing L2 residual norms with variance-aware Mahalanobis scoring on autoencoder outputs.
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Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation
Frozen ViT embeddings in chest radiography suppress small-lesion signal at the CLS token but recover it via patch-local pooling on the same forward pass across multiple models and large cohorts.
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ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification
ORACLE-CT improves CT classification performance by using anatomy-specific support pooling based on multi-organ segmentation, showing gains in AUROC on internal and external datasets.
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Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
DINOv3 at 512x512 resolution with ConvNeXt-B outperforms prior initializations for adult chest X-ray classification but shows no benefit in pediatric cohorts or at 1024 resolution.
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A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world
Large-scale real-world study demonstrates that bottlenecking pre-trained ViT encoders to a scalar per patch produces interpretable affordance features useful for navigation policies.