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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)

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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)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)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)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)