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Classification of melanoma lesions using sparse coded features and random forests., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850C. SPIE, (2016)Discrimination of retinal images containing bright lesions using sparse coded features and SVM., , and . Comput. Biol. Medicine, (2015)Tackling the Problem of Data Imbalancing for Melanoma Classification., , , , , , and . BIOIMAGING, page 32-39. SciTePress, (2016)Adaptive Feature Selection for Object Tracking with Particle Filter., , , and . ICIAR (2), volume 8815 of Lecture Notes in Computer Science, page 395-402. Springer, (2014)Tackling the Problem of Data Imbalancing for Melanoma Classification., , , , , , and . BIOIMAGING, page 32-39. SciTePress, (2016)Special Section Guest Editorial: Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems., and . J. Electronic Imaging, 24 (6): 061101 (2015)Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data., , , , and . Neurocomputing, (November 2023)Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography., , , , , , and . Comput. Methods Programs Biomed., (2019)An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images., , , , , , , , , and 1 other author(s). Comput. Methods Programs Biomed., (2017)Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research., , , , , , , , and . Data, 6 (2): 14 (2021)