activity
20172022
most citedWhen Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation

40 citations · 102 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2022

Distance Map Supervised Landmark Localization for MR-TRUS Registration

Xinrui Song, Xuanang Xu, Sheng Xu +4

In this work, we propose to explicitly use the landmarks of prostate to guide the MR-TRUS image registration. We first train a deep neural network to automatically localize a set o…

cs.CV20216 cited

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…

eess.IV202137 cited

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

Yingda Xia, Dong Yang, Wenqi Li +15

Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standar…

eess.IV2021

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

Ugur Demir, Ismail Irmakci, Elif Keles +7

Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradi…

eess.IV202011 cited

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan

Dong Yang, Ziyue Xu, Wenqi Li +17

The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able t…

cs.CV20205 cited

Multi-Domain Image Completion for Random Missing Input Data

Liyue Shen, Wentao Zhu, Xiaosong Wang +9

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-par…