Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Artificial intelligence, Computer science, Medicine, Mammography, Convolutional neural network, and Deep learning.
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis
Improving breast cancer diagnostics with deep learning for MRI
Differences between human and machine perception in medical diagnosis
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
A convolutional neural network for common coordinate registration of high-resolution histology images
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning
Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives
Machine learning in breast MRI
New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Breast Density Classification with Deep Convolutional Neural Networks