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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service.

, , , , , , , , , , , и . CoRR, (2023)

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D Riethmacher

 

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Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models., , , , , , , , , и 3 other автор(ы). Medical Imaging: Computer-Aided Diagnosis, том 12465 из SPIE Proceedings, SPIE, (2023)Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning., , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 1095039. SPIE, (2019)Effect of observer variability and training cases on U-Net segmentation performance., , , , , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 11316 из SPIE Proceedings, стр. 113160T. SPIE, (2020)Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans., , , , , , , , , и 1 other автор(ы). Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI., , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 11597 из SPIE Proceedings, SPIE, (2021)Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx., , , , , , , , , и 1 other автор(ы). CoRR, (2023)Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT., , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 11314 из SPIE Proceedings, SPIE, (2020)APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service., , , , , , , , , и 2 other автор(ы). e-Science, стр. 1-4. IEEE, (2023)Application and generalizability of U-Net segmentation of immune cells in inflamed tissue., , , , , , и . Medical Imaging: Digital Pathology, том 11603 из SPIE Proceedings, SPIE, (2021)