Xiaoxiang Han is a Ph.D. student with (the SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science), School of Communication and Information Engineering, Shanghai University, Shanghai, P.R.China. His supervisors are Prof. Qi Zhang and Prof. Shugong Xu. His main research interests include medical image/video analysis, weakly/semi/self-supervised learning, computer vision and pattern recognition.
PhD in Information and Communication Engineering
Shanghai University
MEng in Electronic Information, 2024
University of Shanghai for Science and Technology
BEng in Computer Science and Technology, 2021
Jinling Institute of Technology
Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33±3.13 AUC, 84.95±3.88 accuracy, 85.70±4.91 sensitivity, 81.51±8.15 specificity, and 81.99±5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.