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Robert M Nishikawa

Showing results (1-10 of 69) with videos related to

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Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|March 28, 2007
Current status and future directions of computer-aided diagnosis in mammographyRobert M Nishikawa
Medical Physics|May 16, 2006
Computer-aided detection, in its present form, is not an effective aid for screening mammography. For the propositionRobert M Nishikawa
IEEE Transactions on Medical Imaging|August 30, 2021
Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated MammogramsJuhun Lee, Robert M Nishikawa
IEEE Access : Practical Innovations, Open Solutions|March 8, 2021
Cross-organ, cross-modality transfer learning: feasibility study for segmentation and classificationJuhun Lee, Robert M Nishikawa
Medical Physics|September 13, 2006
Identification of simulated microcalcifications in white noise and mammographic backgroundsIngrid Reiser, Robert M Nishikawa
Breast Cancer Research : BCR|February 2, 2024
Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion removerJuhun Lee, Robert M Nishikawa
Journal of Breast Imaging|November 29, 2024
Stop Training Artificial Intelligence Algorithms Now. Start Prospective Trials!Robert M Nishikawa, Alisa Sumkin
Journal of Imaging Informatics in Medicine|December 16, 2025
Mammo-GAN-Assisted Deep Network Training Scheme for Lesion DetectionJuhun Lee, Robert M Nishikawa
Medical Physics|January 25, 2018
Automated mammographic breast density estimation using a fully convolutional networkJuhun Lee, Robert M Nishikawa
Academic Radiology|August 4, 2014
CADe for early detection of breast cancer-current status and why we need to continue to explore new approachesRobert M Nishikawa, David Gur
Pageof 7

Showing results (1-10 of 69) with videos related to

Sort By:
Pageof 7
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|March 28, 2007
Current status and future directions of computer-aided diagnosis in mammographyRobert M Nishikawa
Medical Physics|May 16, 2006
Computer-aided detection, in its present form, is not an effective aid for screening mammography. For the propositionRobert M Nishikawa
IEEE Transactions on Medical Imaging|August 30, 2021
Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated MammogramsJuhun Lee, Robert M Nishikawa
IEEE Access : Practical Innovations, Open Solutions|March 8, 2021
Cross-organ, cross-modality transfer learning: feasibility study for segmentation and classificationJuhun Lee, Robert M Nishikawa
Medical Physics|September 13, 2006
Identification of simulated microcalcifications in white noise and mammographic backgroundsIngrid Reiser, Robert M Nishikawa
Breast Cancer Research : BCR|February 2, 2024
Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion removerJuhun Lee, Robert M Nishikawa
Journal of Breast Imaging|November 29, 2024
Stop Training Artificial Intelligence Algorithms Now. Start Prospective Trials!Robert M Nishikawa, Alisa Sumkin
Journal of Imaging Informatics in Medicine|December 16, 2025
Mammo-GAN-Assisted Deep Network Training Scheme for Lesion DetectionJuhun Lee, Robert M Nishikawa
Medical Physics|January 25, 2018
Automated mammographic breast density estimation using a fully convolutional networkJuhun Lee, Robert M Nishikawa
Academic Radiology|August 4, 2014
CADe for early detection of breast cancer-current status and why we need to continue to explore new approachesRobert M Nishikawa, David Gur
Pageof 7