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Use of Ultrasonic Parameters as Adjuvant Tool of Diagnosis and Monitoring of Bone Lesions.

, , , , , and . IWBBIO, page 243-250. Copicentro Editorial, (2013)

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Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks., and . Medical Biol. Eng. Comput., 61 (12): 3193-3207 (December 2023)Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images., , , , and . BMC Medical Imaging, 19 (1): 85:1-85:13 (2019)Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network., , , , , and . IEEE Access, (2020)Applying a novelty filter as a matching criterion to iris recognition for binary and real-valued feature vectors., , , and . Signal Image Video Process., 7 (2): 287-296 (2013)A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes., , and . Comput. Methods Programs Biomed., (2018)A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound., and . Comput. Biol. Medicine, (2020)Breast tumor classification in ultrasound images using neural networks with improved generalization methods., , , and . EMBC, page 6321-6325. IEEE, (2015)Ultrasonic Attenuation and Speed in phantoms made of PVCP and Evaluation of acoustic and thermal properties of ultrasonic phantoms made of polyvinyl chloride-plastisol (PVCP)., , , , and . IWBBIO, page 233-241. Copicentro Editorial, (2013)A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography., and . Comput. Biol. Medicine, (2017)BUSAT: A MATLAB Toolbox for Breast Ultrasound Image Analysis., , and . MCPR, volume 10267 of Lecture Notes in Computer Science, page 268-277. Springer, (2017)