Award

Detecting Mammographically-Occult Cancer in Women with Dense Breasts

Description: Over six million women each year receive a notification letter informing them that they have dense breast tissue and that they should consider having supplemental screening with ultrasound or MRI. Unfortunately, there is insufficient information available to women as to the benefits for them as an individual to balance against the known harms of getting supplemental screening. Our research goal is to develop a technique that can detect mammographically occult breast cancer in women with dense breast tissue. This information would help women make informed decisions regarding supplemental screening.

Funder: National Cancer Institute

Funding type: research

Funder scheme: R21

Start date: 2017-03-03

End date: 2019-02-28

Investigator: ROBERT M NISHIKAWA

Institution: UNIVERSITY OF PITTSBURGH AT PITTSBURGH

Funded outputs count: 3

Funder award ID: R21CA216495

OpenAlex ID: G70577981

Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform

2019 · Juhun Lee, Robert M. Nishikawa · Journal of medical imaging · 11

Automated mammographic breast density estimation using a fully convolutional network

2018 · Juhun Lee, Robert M. Nishikawa · Medical Physics · 96

Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case

2017 · Robert M. Nishikawa, Kyongtae T. Bae · Journal of the American College of Radiology · 45