When: Apr 01 2021 @ 3:00 PM

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Coherence-based learning from raw ultrasound data for breast mass diagnosis
Abstract: Breast cancer is the most prevalent cancer among women in the United States, with approximately one in eight women being diagnosed in their lifetimes. Imaging modalities such as mammography, MRI, and ultrasound are employed to non-invasively visualize breast masses in order to determine the need for a biopsy. However, each of these methods results in a significant number of patients requiring biopsies of benign masses. Ultrasound in particular is praised for its low cost, painlessness, and portability, yet the false positive rate of breast ultrasound can be as high as 93% depending on the type of mass in question. Most commonly, diagnosis is performed using the brightness-mode (B-mode) image present on most clinical ultrasound scanners, which transitions naturally to the use of B-mode images for segmentation and classification of breast masses. Ultimately, segmentation and classification of breast masses can be summarized as analysis of a grayscale image. While this approach has been successful, information is lost during the B-mode image formation process.
An alternative approach to the lossy process of information extraction from B-mode images is to leverage features (e.g., spatial coherence) of backscattered ultrasound waves to determine the content of a breast mass. I will first describe my contributions to improve the diagnostic quality of breast ultrasound images by leveraging spatial coherence information. Next, I will present my deep learning approach to overcome limitations with real-time implementation of coherence-based imaging techniques. Finally, I will present a new method to learn the high-dimensional features encoded within backscattered ultrasound waves in order to differentiate benign from malignant breast masses.
Committee Members

Muyinatu Bell, Department of Electrical and Computer Engineering
Vishal Patel, Department of Electrical and Computer Engineering
Najim Dehak, Department of Electrical and Computer Engineering