39 citations · 66 across the 6 of their papers we have counts for
6 papers
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
Jian Shi, Pengyi Zhang, Ni Zhang +2
Medical imaging often contains critical fine-grained features, such as tumors or hemorrhages, crucial for diagnosis yet potentially too subtle for detection with conventional metho…
VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios
Huanzhang Dou, Wenhu Zhang, Pengyi Zhang +6
With the motivation of practical gait recognition applications, we propose to automatically create a large-scale synthetic gait dataset (called VersatileGait) by a game engine, whi…
FcaNet: Frequency Channel Attention Networks
Zequn Qin, Pengyi Zhang, Fei Wu +1
Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms w…
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
Pengyi Zhang, Yunxin Zhong, Yulin Deng +2
Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination. We propose a novel approach, called DRR4Covid, to lea…
Learning Diagnosis of COVID-19 from a Single Radiological Image
Pengyi Zhang, Yunxin Zhong, Xiaoying Tang +2
Radiological image is currently adopted as the visual evidence for COVID-19 diagnosis in clinical. Using deep models to realize automated infection measurement and COVID-19 diagnos…
ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation
Pengyi Zhang, Yunxin Zhong, Xiaoqiong Li
We propose adversarial constrained-CNN loss, a new paradigm of constrained-CNN loss methods, for weakly supervised medical image segmentation. In the new paradigm, prior knowledge…