Breast: Page 158

U.K. group studes link between cancer detection, age, and breast density
By Brian Casey
Sunday, November 26 | 11:05 a.m.-11:15 a.m. | SSA01-03 | Arie Crown TheaterIn this Sunday morning talk, researchers from the U.K. will discuss their investigation of the relationship between breast density, age, and cancer detection rates in a large breast screening program.
November 8, 2017
Breast MRI neural networks predict recurrence scores
By Kate Madden Yee
Friday, December 1 | 11:20 a.m.-11:30 a.m. | SST01-06 | Room E450BResearchers in New York have found that deep-learning networks can be trained with breast MRI data to predict Oncotype DX recurrence scores.
November 8, 2017
Deep-learning method creates super-resolution mammo images
By Wayne Forrest
Thursday, November 30 | 12:15 p.m.-12:45 p.m. | PH257-SD-THA3 | Lakeside, PH Community, Station 3In this poster presentation, Japanese researchers will present a deep-learning technique that enhances mammography results to create super-resolution images.
November 8, 2017
2017 11 07 18 37 9362 Roadies Ribbon 400
Machine-learning method shows promise for assessing breast cancer risk
By Wayne Forrest
Wednesday, November 29 | 11:40 a.m.-11:50 a.m. | SSK02-08 | Room E451AIn this session, researchers will present a machine-learning method for analyzing parenchymal patterns from digital mammography screening to assess breast cancer risk and how it may change over time.
November 8, 2017
Breast parenchymal patterns aid breast cancer evaluation
By Wayne Forrest
Wednesday, November 29 | 11:30 a.m.-11:40 a.m. | SSK02-07 | Room E451AUsing automated measurements of breast parenchymal patterns derived from synthesized digital breast tomosynthesis exams could help assess cancer risk, according to researchers from the University of Pennsylvania.
November 8, 2017
AI-based software could reduce unnecessary biopsies
By Wayne Forrest
Wednesday, November 29 | 11:20 a.m.-11:30 a.m. | SSK01-06 | Room E450AA group from the University of Southern California will discuss the performance of a quantitative computer-aided detection software application that uses deep learning and artificial intelligence (AI) to improve mammography screening.
November 8, 2017
Novel deep-learning process aids breast cancer detection
By Wayne Forrest
Wednesday, November 29 | 11:10 a.m.-11:20 a.m. | SSK02-05 | Room E451AIn this talk, researchers will present a method of combining routine clinical data and natural language processing to create large datasets for deep learning and enhancing breast cancer diagnosis.
November 8, 2017
Deep-learning CAD adds to breast ultrasound findings
By Wayne Forrest
Wednesday, November 29 | 10:50 a.m.-11:00 a.m. | SSK02-03 | Room E451AThe potential benefits of deep-learning computer-aided detection (CAD) for breast ultrasound will be outlined in this Wednesday morning presentation by researchers from South Korea.
November 8, 2017
Deep learning could lend a hand with digital mammo
By Wayne Forrest
Wednesday, November 29 | 10:40 a.m.-10:50 a.m. | SSK02-02 | Room E451AA computer system based on deep-learning technology was as efficient as radiologists in detecting breast cancer on digital mammography exams in a study by a group from Sweden and the Netherlands.
November 8, 2017
Deep-learning software improves breast US performance
By Kate Madden Yee
Tuesday, November 28 | 3:40 p.m.-3:50 p.m. | SSJ02-05 | Room E450AWhen it comes to identifying breast cancer, deep-learning software for breast ultrasound achieves diagnostic accuracy comparable to that of radiologists, Swiss researchers have found.
November 8, 2017
CAD boosts performance of contrast spectral mammography
By Kate Madden Yee
Tuesday, November 28 | 12:15 p.m.-12:45 p.m. | BR234-SD-TUA2 | Lakeside, BR Community, Station 2Computer-aided diagnosis of contrast-enhanced spectral mammography (CAD-CESM) can help reduce false positives, according to this Tuesday afternoon poster presentation.
November 8, 2017
Deep learning improves mammography multitasking
By Wayne Forrest
Tuesday, November 28 | 11:30 a.m.-11:40 a.m. | SSG13-07 | Room S404ABEveryone can use some help when multitasking. Researchers from the University of Michigan will present the benefits to mammography from a deep convolutional neural network based on a multitask transfer learning algorithm.
November 8, 2017
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