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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.

, , , , , и . Bildverarbeitung für die Medizin, стр. 204. Springer, (2023)

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Georg Packhäuser

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Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems., , , и . CoRR, (2022)Abstract: Utility-preserving Measure for Patient Privacy - Deep Learning-based Anonymization of Chest Radiographs., , , , и . Bildverarbeitung für die Medizin, стр. 363. Springer, (2024)Is Medical Chest X-ray Data Anonymous?, , , , , и . 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., , , , , и . Bildverarbeitung für die Medizin, стр. 204. Springer, (2023)Privacy-enhancing Image Sampling for the Synthesis of High-quality Anonymous Chest Radiographs., , , , и . Bildverarbeitung für die Medizin, стр. 27-32. Springer, (2024)Using Forestnets for Partial Fine-Tuning Prior to Breast Cancer Detection in Ultrasounds., , , , и . ISBI, стр. 1-5. IEEE, (2023)Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy., , , , и . CoRR, (2022)Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning., , , , , , , , и . EMBC, стр. 1-4. IEEE, (2023)Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems., , , и . ISBI, стр. 1-5. IEEE, (2023)Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy., , , , и . MICCAI (3), том 14222 из Lecture Notes in Computer Science, стр. 262-272. Springer, (2023)