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

, , , , , and . Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)

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Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis., , and . CEC, page 1436-1442. IEEE, (2000)Self-organizing maps for analyzing mammographic findings., and . ICNN, page 2472-2474. IEEE, (1997)Ipsilateral Lesion Detection Refinement for Tomosynthesis., , , , , , , , and . IEEE Trans. Medical Imaging, 42 (10): 3080-3090 (October 2023)Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT., , , , and . Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features., , , and . Image Processing, volume 2710 of SPIE Proceedings, SPIE, (1996)Application of support vector machines to breast cancer screening using mammogram and clinical history data., , , , , and . Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers., , and . Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)Assessment of Low Energies and Slice Depth in the Quantification of Breast Tomosynthesis., , , , , and . Digital Mammography / IWDM, volume 5116 of Lecture Notes in Computer Science, page 530-536. Springer, (2008)Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal., and . BIBE, page 1-6. IEEE, (2008)Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes., , , , , , and . CoRR, (2020)