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Disambiguation of "Fuhrman, Jordan D."

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Effect of observer variability and training cases on U-Net segmentation performance.

, , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 11316 of SPIE Proceedings, page 113160T. SPIE, (2020)

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D Zeppenfeld

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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., , , , , , , , , and 3 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 12465 of SPIE Proceedings, SPIE, (2023)Application and generalizability of U-Net segmentation of immune cells in inflamed tissue., , , , , , and . Medical Imaging: Digital Pathology, volume 11603 of SPIE Proceedings, SPIE, (2021)APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service., , , , , , , , , and 2 other author(s). e-Science, page 1-4. IEEE, (2023)Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 1095039. SPIE, (2019)Effect of observer variability and training cases on U-Net segmentation performance., , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 11316 of SPIE Proceedings, page 113160T. SPIE, (2020)Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans., , , , , , , , , and 1 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx., , , , , , , , , and 1 other author(s). CoRR, (2023)