activity
20192022
most citedDATR: Domain-adaptive transformer for multi-domain landmark detection

6 citations · 9 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2022

Information-guided pixel augmentation for pixel-wise contrastive learning

Quan Quan, Qingsong Yao, Jun Li +1

Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel…

cs.CV20226 cited

DATR: Domain-adaptive transformer for multi-domain landmark detection

Heqin Zhu, Qingsong Yao, S. Kevin Zhou

Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based o…

eess.IV2022

Rib Suppression in Digital Chest Tomosynthesis

Yihua Sun, Qingsong Yao, Yuanyuan Lyu +4

Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremel…

cs.CV20211 cited

One-Shot Medical Landmark Detection

Qingsong Yao, Quan Quan, Li Xiao +1

The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medica…

cs.CV2020

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

Qingsong Yao, Zecheng He, Yi Lin +3

Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical A…

eess.IV2020

Label-Free Segmentation of COVID-19 Lesions in Lung CT

Qingsong Yao, Li Xiao, Peihang Liu +1

Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herei…