1.9k citations · 6.7k across the 80 of their papers we have counts for
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Disrupting Adversarial Transferability in Deep Neural Networks
Christopher Wiedeman, Ge Wang
Adversarial attack transferability is well-recognized in deep learning. Prior work has partially explained transferability by recognizing common adversarial subspaces and correlati…
Stationary Multi-source AI-powered Real-time Tomography (SMART)
Weiwen Wu, Yaohui Tang, Tianling Lv +6
Over the past decades, the development of CT technologies has been largely driven by the needs for cardiac imaging but the temporal resolution remains insufficient for clinical CT…
Cross-modal Attention for MRI and Ultrasound Volume Registration
Xinrui Song, Hengtao Guo, Xuanang Xu +6
Prostate cancer biopsy benefits from accurate fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years, convolutional neural networks (CNNs…
AI-Enabled Ultra-Low-Dose CT Reconstruction
Weiwen Wu, Chuang Niu, Shadi Ebrahimian +3
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. Wi…
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Arjun Krishna, Kedar Bartake, Chuang Niu +4
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds w…
Task-Oriented Low-Dose CT Image Denoising
Jiajin Zhang, Hanqing Chao, Xuanang Xu +3
The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, whi…