CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients
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TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.
Introduces the Early-QaTa-COV19 dataset and reports that CSEN reaches over 97% sensitivity and over 95.5% specificity for early COVID-19 detection from X-rays.
Noise injection during training reduces the ID-OOD performance gap in COVID-19 CXR classification from 0.10-0.20 to 0.01-0.06.
citing papers explorer
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CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
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TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.
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Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Introduces the Early-QaTa-COV19 dataset and reports that CSEN reaches over 97% sensitivity and over 95.5% specificity for early COVID-19 detection from X-rays.
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Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets
Noise injection during training reduces the ID-OOD performance gap in COVID-19 CXR classification from 0.10-0.20 to 0.01-0.06.