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Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment.

, , , , , , , , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 676-684. Springer, (2019)

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Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks., , , , , , and . CoRR, (2018)Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment., , , , , , and . CoRR, (2021)Abstract: Is Medical Chest X-ray Data Anonymous? - Deep Learning-based Patient Re-identification is Able to Exploit the Biometric Nature of Medical Chest X-ray Data., , , , , and . Bildverarbeitung für die Medizin, page 204. Springer, (2023)Epoch-wise label attacks for robustness against label noise., and . CoRR, (2019)Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels., , , , , , and . CoRR, (2019)Abstract: Utility-preserving Measure for Patient Privacy - Deep Learning-based Anonymization of Chest Radiographs., , , , and . Bildverarbeitung für die Medizin, page 363. Springer, (2024)Abstract: Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation., , , , and . Bildverarbeitung für die Medizin, page 288. Springer, (2021)Recognition of Similarities and Abnormalities in Chest Radiographs (Erkennung von Ähnlichkeiten und Abnormalitäten in Thoraxröntgenbildern). University of Erlangen-Nuremberg, Germany, (2023)base-search.net (ftuniverlangen:oai:ub.uni-erlangen.de-opus:23817).Is Medical Chest X-ray Data Anonymous?, , , , , and . CoRR, (2021)Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment., , , , , , , , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 676-684. Springer, (2019)