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Application of support vector machines to breast cancer screening using mammogram and clinical history data.

, , , , , и . Image Processing, том 5032 из SPIE Proceedings, SPIE, (2003)

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Другие публикации лиц с тем же именем

Self-organizing maps for analyzing mammographic findings., и . ICNN, стр. 2472-2474. IEEE, (1997)Ipsilateral Lesion Detection Refinement for Tomosynthesis., , , , , , , , и . IEEE Trans. Medical Imaging, 42 (10): 3080-3090 (октября 2023)Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis., , и . CEC, стр. 1436-1442. IEEE, (2000)Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT., , , , и . Computer-Aided Diagnosis, том 12033 из SPIE Proceedings, SPIE, (2022)Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features., , , и . Image Processing, том 2710 из SPIE Proceedings, SPIE, (1996)Application of support vector machines to breast cancer screening using mammogram and clinical history data., , , , , и . Image Processing, том 5032 из SPIE Proceedings, SPIE, (2003)Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers., , и . Image Processing, том 5032 из SPIE Proceedings, SPIE, (2003)Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes., , , , , , и . CoRR, (2020)Application of artificial neural networks for diagnosis of breast cancer., и . CEC, стр. 1755-1759. IEEE, (1999)Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal., и . BIBE, стр. 1-6. IEEE, (2008)