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Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images.

, , , , , , and . IEEE Trans. Medical Imaging, 34 (4): 962-973 (2015)

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Towards automatic pulmonary nodule management in lung cancer screening with deep learning., , , , , , , , , and 3 other author(s). CoRR, (2016)Learning to quantify emphysema extent: What labels do we need?, , , , and . CoRR, (2018)Learning to Quantify Emphysema Extent: What Labels Do We Need?, , , , and . IEEE J. Biomed. Health Informatics, 24 (4): 1149-1159 (2020)Detecting emphysema with multiple instance learning., , , , and . ISBI, page 510-513. IEEE, (2018)Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Trees., , , , , , and . IPMI, volume 7917 of Lecture Notes in Computer Science, page 74-85. Springer, (2013)Automatic detection of spiculation of pulmonary nodules in computed tomography images., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 941409. SPIE, (2015)Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem., , , , , , , , and . IEEE Trans. Medical Imaging, 39 (4): 854-865 (2020)A Hierarchical Scheme for Geodesic Anatomical Labeling of Airway Trees., , , , , , , and . MICCAI (3), volume 7512 of Lecture Notes in Computer Science, page 147-155. Springer, (2012)Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images., , , , , , and . IEEE Trans. Medical Imaging, 34 (4): 962-973 (2015)Deep Learning from Label Proportions for Emphysema Quantification., , , , , , , and . MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 768-776. Springer, (2018)