|
|||||
|
|
Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms |
|
| Authors: | Wu Nan Huang Zhe Shen Yiqiu Park Jungkyu Phang Jason Makino Taro Gene Kim S. Cho Kyunghyun Heacock Laura Moy Linda Geras Krzysztof J. |
| Affiliation: | 1.Center for Data Science, New York University, New York City, USA ;2.Department of Radiology, New York University School of Medicine, New York City, USA ;3.Perlmutter Cancer Center, NYU Langone Health, New York City, USA ;4.Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York City, USA ;5.Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York City, USA ;6.CIFAR Associate Fellow, New York City, USA ; |
| Abstract: | Journal of Digital Imaging - Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to... |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |