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W N Street

Showing results (1-10 of 12) with videos related to

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IEEE Transactions on Neural Networks|February 2, 2008
An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognitionKyoung-Mi Lee, W N Street
Evolutionary Computation|June 8, 2000
Efficient and scalable Pareto optimization by evolutionary local selection algorithmsF Menczer, M Degeratu, W N Street
Cancer Letters|March 15, 1994
Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspiratesW H Wolberg, W N Street, O L Mangasarian
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|December 10, 1999
Importance of nuclear morphology in breast cancer prognosisW H Wolberg, W N Street, O L Mangasarian
Journal of Neuroscience Methods|September 1, 1994
A neural network-based spike discriminatorJ S Oghalai, W N Street, W S Rhode
Analytical and Quantitative Cytology and Histology|April 1, 1995
Image analysis and machine learning applied to breast cancer diagnosis and prognosisW H Wolberg, W N Street, O L Mangasarian
Analytical and Quantitative Cytology and Histology|December 1, 1993
Breast cytology diagnosis with digital image analysisW H Wolberg, W N Street, O L Mangasarian
Cancer|June 25, 1997
Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosisW H Wolberg, W N Street, O L Mangasarian
Human Pathology|July 1, 1995
Computer-derived nuclear features distinguish malignant from benign breast cytologyW H Wolberg, W N Street, D M Heisey, et al.
Analytical and Quantitative Cytology and Histology|August 1, 1995
Computer-derived nuclear "grade" and breast cancer prognosisW H Wolberg, W N Street, D M Heisey, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
IEEE Transactions on Neural Networks|February 2, 2008
An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognitionKyoung-Mi Lee, W N Street
Evolutionary Computation|June 8, 2000
Efficient and scalable Pareto optimization by evolutionary local selection algorithmsF Menczer, M Degeratu, W N Street
Cancer Letters|March 15, 1994
Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspiratesW H Wolberg, W N Street, O L Mangasarian
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|December 10, 1999
Importance of nuclear morphology in breast cancer prognosisW H Wolberg, W N Street, O L Mangasarian
Journal of Neuroscience Methods|September 1, 1994
A neural network-based spike discriminatorJ S Oghalai, W N Street, W S Rhode
Analytical and Quantitative Cytology and Histology|April 1, 1995
Image analysis and machine learning applied to breast cancer diagnosis and prognosisW H Wolberg, W N Street, O L Mangasarian
Analytical and Quantitative Cytology and Histology|December 1, 1993
Breast cytology diagnosis with digital image analysisW H Wolberg, W N Street, O L Mangasarian
Cancer|June 25, 1997
Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosisW H Wolberg, W N Street, O L Mangasarian
Human Pathology|July 1, 1995
Computer-derived nuclear features distinguish malignant from benign breast cytologyW H Wolberg, W N Street, D M Heisey, et al.
Analytical and Quantitative Cytology and Histology|August 1, 1995
Computer-derived nuclear "grade" and breast cancer prognosisW H Wolberg, W N Street, D M Heisey, et al.
Pageof 2