Zobrazeno 1 - 10
of 311
pro vyhledávání: '"Robert M. Nishikawa"'
Autor:
Juhun Lee, Robert M. Nishikawa
Publikováno v:
Breast Cancer Research, Vol 26, Iss 1, Pp 1-16 (2024)
Abstract Background The wide heterogeneity in the appearance of breast lesions and normal breast structures can confuse computerized detection algorithms. Our purpose was therefore to develop a Lesion Highlighter (LH) that can improve the performance
Externí odkaz:
https://doaj.org/article/47cb2935cb8840eb8834031865426ad8
Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification
Autor:
Juhun Lee, Robert M. Nishikawa
Publikováno v:
IEEE Access, Vol 8, Pp 210194-210205 (2020)
We conducted two analyses by comparing the transferability of a traditionally transfer-learned CNN (TL) to that of a CNN fine-tuned with an unrelated set of medical images (mammograms in this study) first and then fine-tuned a second time using TL, w
Externí odkaz:
https://doaj.org/article/f4e7b7866c364bed9a48ebcffe4d30f5
Autor:
Karem D. Marcomini, Eduardo F. C. Fleury, Vilmar M. Oliveira, Antonio A. O. Carneiro, Homero Schiabel, Robert M. Nishikawa
Publikováno v:
Bioengineering, Vol 5, Iss 3, p 62 (2018)
Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant a
Externí odkaz:
https://doaj.org/article/6d3c1aa15f4a4209b423e3b1e2665bab
Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer
Publikováno v:
International Journal of Biomedical Imaging, Vol 2012 (2012)
We propose a regularization based approach for case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to improve the classification accuracy on a query case by making use of a set of similar cases retrieved from
Externí odkaz:
https://doaj.org/article/9ab8d6e81eda495da19eafa021affcab
Autor:
Sarah M Friedewald, David V Schacht, Golbahar Houshmand, Robert M Nishikawa, Michael N Linver
Publikováno v:
Journal of Breast Imaging. 4:520-529
Feedback to physicians on their clinical performance is critical to continuous learning and maintenance of skills as well as maintaining patient safety. However, it is fraught with challenges around both implementation and acceptance. Additionally, r
Autor:
Jung Hyun Yoon, Fredrik Strand, Pascal A. T. Baltzer, Emily F. Conant, Fiona J. Gilbert, Constance D. Lehman, Elizabeth A. Morris, Lisa A. Mullen, Robert M. Nishikawa, Nisha Sharma, Ilse Vejborg, Linda Moy, Ritse M. Mann
Publikováno v:
Radiology, 307, 5
Item does not contain fulltext
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aaa77f5691e7e1964f844c01611c259
https://repository.ubn.ru.nl/handle/2066/294221
https://repository.ubn.ru.nl/handle/2066/294221
Publikováno v:
Medical physicsREFERENCES. 49(12)
Due to the complex nature of digital breast tomosynthesis (DBT) in imaging techniques, reading times are longer than 2D mammograms. A robust computer-aided diagnosis system in DBT could help radiologists reduce their workload and reading times.The pu
Autor:
Robert M. Nishikawa, Christoph Hoeschen, Anant Madabhushi, Kyle J. Myers, Thomas M. Deserno, Elizabeth A. Krupinski, Ronald M. Summers, Claudia R. Mello-Thoms, Matthew A. Kupinski, Jeffrey H. Siewerdsen
Publikováno v:
Journal of Medical Imaging. 9
Autor:
Juhun Lee, Robert M. Nishikawa
Publikováno v:
Medical Imaging 2022: Computer-Aided Diagnosis.
Publikováno v:
Medical Imaging 2022: Computer-Aided Diagnosis.