Geometric normalization of lung shapes via automatic landmark detection and piecewise affine warping yields 98.60% accuracy for COVID-19 and pneumonia classification while showing greater resistance to acquisition artifacts than unaligned images.
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
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Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
DMDSC adapts class-specific margins dynamically by label frequency in deep simplex classifiers to improve open-set recognition on imbalanced medical image datasets.
citing papers explorer
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Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping
Geometric normalization of lung shapes via automatic landmark detection and piecewise affine warping yields 98.60% accuracy for COVID-19 and pneumonia classification while showing greater resistance to acquisition artifacts than unaligned images.
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Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
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DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets
DMDSC adapts class-specific margins dynamically by label frequency in deep simplex classifiers to improve open-set recognition on imbalanced medical image datasets.