Who: Dr. Hassan S. Salehi, Assistant Professor, Department of Electrical & Computer Engineering, CSU, Chico
When: Friday, Oct 26, 2018; 4pm
Where: Holt Hall 170

Ovarian cancer ranks fifth as the cause of cancer death in women. Due to nonspecific associated symptoms as well as lack of efficacious screening techniques at the disposal of patients, the survival rate for ovarian cancer has not significantly improved over the last two decades. Therefore, ovarian cancer has the highest mortality rate of all gynecologic cancers. As a result, there is an urgent need to improve the current diagnostic techniques to detect early malignancies in the ovary. Here, I will describe the development of a novel real-time co-registered photoacoustic/ultrasound (PAT/US) prototype imaging system along with machine learning techniques as a future screening modality for early-stage ovarian cancer detection and characterization. Further information on low-cost photoacoustic microscopy system with a novel laser scanner will be discussed.

Dental caries is a prominent health problem that affects more than 90 percent of all dentate adults and more than two-thirds of children in the United States. The conventional approach for diagnosing caries is clinical examination and supplemented by radiographs. However, studies based on the clinical and radiographic examination methods often show low sensitivity. To address this challenge, we have introduced a novel approach combining deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for classification of human oral tissues to detect early dental caries. The proposed technique was validated on ex vivo OCT images of human oral tissues, which attested to effectiveness of the proposed method. The sensitivity and specificity of distinguishing between different oral tissues were found to be ~98% and 100%, respectively. These preliminary results demonstrate the feasibility of using deep learning algorithms with OCT images to perform the automated diagnosis of early dental caries.