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Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images.

, , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750O. SPIE, (2018)

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Automatic detection of basal cell carcinoma using vascular-extracted features from dermoscopy images., , , and . CCECE, page 1-4. IEEE, (2016)A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning., , , , and . J. Medical Systems, 42 (2): 33:1-33:11 (2018)Automatic detection of translucency using a deep learning method from patches of clinical basal cell carcinoma images., , , , , and . APSIPA, page 685-688. IEEE, (2018)Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750O. SPIE, (2018)Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94143M. SPIE, (2015)Distill-Quantize-Tune - Leveraging Large Teachers for Low-Footprint Efficient Multilingual NLU on Edge., , , and . ICASSP, page 1-5. IEEE, (2023)Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification., , , , and . IEEE J. Biomed. Health Informatics, 21 (6): 1675-1684 (2017)Pyramid Dynamic Inference: Encouraging Faster Inference Via Early Exit Boosting., , , , , and . ICASSP, page 1-5. IEEE, (2023)