19 citations · 21 across the 4 of their papers we have counts for
7 papers
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Bin Wang, Hongyi Pan, Armstrong Aboah +7
Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as…
SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification
Michael S. Yao, Allison Chae, Matthew T. MacLean +8
Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visi…
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar, Ismail Irmakci, Drew A. Torigian +5
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and rese…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Deep Learning for Musculoskeletal Image Analysis
Ismail Irmakci, Syed Muhammad Anwar, Drew A. Torigian +1
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and…
Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
Rodney LaLonde, Drew Torigian, Ulas Bagci
Convolutional neural network based systems have largely failed to be adopted in many high-risk application areas, including healthcare, military, security, transportation, finance,…