Soheyla Amirian

The University of Georgia

Interested in Image Processing, Deep Learning, Image Captioning, Video Captioning, Machine Learning, Computer Graphics, Fairness in deep learning models, Computer Networks, and Instructional Technology. Highly motivated and eager to learn new things. Strong motivational and leadership skills. Ability to work as an individual as well as in a group.

Explainable Deep Few-Shot Learning in Knee Radiography Analysis

Rapid yet, deep learning medical image analysis has already shown success in a variety of knee image analysis tasks, ranging from knee joint area localization to joint space segmentation and measurement, with almost a human-like performance. However, there are several fundamental challenges that stop deep learning methods to obtain their full potential in a clinical setting such as orthopedics. These include the need for a large number of gold-standard, manually annotated training images and a lack of explainability and interpretability. To address these challenges, this study is the first to present an explainable deep few-shot learning model that can localize the knee joint area and segment the joint space in plain knee radiographs, using only a small number of manually annotated radiographs.

Word Embedding Neural Networks to Advance Knee Osteoarthritis Research

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.