Deep Learning and Total Body DXA Scans (The TBDXA.I. Project)
In this project, we attempt to use deep learning methods on total body DXA scans to extract more information than was previously done, and thus, providing more accurate predictions of clinical outcomes, including cardiovascular disease (CVD), CVD death, overall mortality, cancer, cancer death, hip fracture, physical disability, incident insulin-resistant diabetes, and severity of insulin resistance. We will use a novel new approach called self-supervised learning to extract features from the DXA whole body images collected in the Health, Aging and Body Composition (Health ABC) Study.
Hawaiʻi and Pacific Islands Mammography Registry
The Hawaiʻi & Pacific Islands Mammography Registry (HIPIMR) database aims to maintain a computerized database of women undergoing breast imaging in the state of Hawaiʻi. It will include demographic, clinical and risk factor information, breast imaging interpretations, cancer outcomes, and vital status obtained through linkage with the Hawai’i Tumor Registry (HTR) and Hawaii State Department of Health and Vital Records (HSDHVR), respectively.
SMART Melanoma Project
In the Systematic Melanoma Assessment and Risk Triaging (SMART) project, our goal is to establish a deep learning computer vision (DLCV) method to triage lesions appropriate for biopsy while providing a platform for increased vigilance of benign lesions. Hawaii’s multiethnic populations, who experience year-round ultraviolet radiation (UVR) exposure and therefore higher risks for melanoma, provide a unique opportunity to identify ways to reduce the burden of this disease.
Three Compartment Breast Lesion Detection Study
The long-term goal of this project is to determine if biological diagnostic measures of mammographic lesions can be used to improve current CADe algorithms in estimating the probability of breast cancer. Our objective was to quantify lipid-protein-water signatures of mammographically suspicious breast lesions to better predict malignant findings. Our central hypothesis was that novel lipid-protein-water image biomarkers can be combined with existing QIA/radiomics methods to improve the sensitivity and specificity of cancer diagnosis and reduce the number of unnecessary biopsies.