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On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis.

, , , , , and . IEEE Trans. Medical Imaging, 35 (4): 1013-1024 (2016)

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On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis., , , , , and . IEEE Trans. Medical Imaging, 35 (4): 1013-1024 (2016)Clavicle segmentation in chest radiographs., , , , and . Medical Image Anal., 16 (8): 1490-1502 (2012)JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction., , , , and . CoRR, (2023)Interactive classification of lung tissue in CT scans by combining prior and interactively obtained training data: A simulation study., , , , , , , , and . ICPR, page 105-108. IEEE Computer Society, (2012)Mammary duct detection using self-supervised encoders., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)A survey on deep learning in medical image analysis., , , , , , , , and . Medical Image Anal., (2017)Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration., , , , , , , and . CoRR, (2019)Fovea detection in optical coherence tomography using convolutional neural networks., , , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 1013302. SPIE, (2017)Multiple-instance learning for computer-aided detection of tuberculosis., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 90351J. SPIE, (2014)Automatic Image Processing Algorithm to Detect Hard Exudates based on Mixture Models., , , , and . EMBC, page 4453-4456. IEEE, (2006)