Transfer Learning for Male Breast Cancer
Principal Investigator
Name
Lawrence Fulton
Degrees
Ph.D. MSStat MHA MS MMAS MSS
Institution
Texas State University
Position Title
Associate Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-742
Initial CDAS Request Approval
Mar 9, 2021
Title
Transfer Learning for Male Breast Cancer
Summary
Men account for just over 1% of breast cancer diagnoses each year. With so few cases, training deep learning models is difficult. Transfer learning, the process by which learning acquired from one domain might inform learning in another, is a technique for analyzing imagery that might be useful for analyzing male breast cancer identification and classification based on knowledge of female breast cancer.
Aims
1. Develop highly specific, highly sensitive, automated, novel, single stage deep learning methods to improve PPV and NPV of screening imagery analysis with known-labels using image pre-processing, Deep Convolutional Neural Networks, and other techniques to address the problem of overdiagnosis and overtreatment.
2. Construct Deep Learning Transfer Models for identification and classification of breast cancer in males using available imagery and data from females.
Collaborators
Dr. Yan Yan, Dr. Alex McCleod, Dr. Barbara Hewitt, Dr. Zo Ramamonjiarevelo, Dr. Diane Dolezel, and Dr. Tahir Ekin