1k citations · 1.2k across the 9 of their papers we have counts for
9 papers
Multi-Scale Prototypical Transformer for Whole Slide Image Classification
Saisai Ding, Jun Wang, Juncheng Li +1
Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accu…
Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images
Jian Wang, Liang Qiao, Shichong Zhou +6
Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automaticCAD system, lesion detection is critical for th…
Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification
Saisai Ding, Juncheng Li, Jun Wang +2
The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness…
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images
Jun Shi, Yuanming Zhang, Zheng Li +4
Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the h…
A channel attention based MLP-Mixer network for motor imagery decoding with EEG
Yanbin He, Zhiyang Lu, Jun Wang +1
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-…
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
Zhiyang Lu, Zheng Li, Jun Wang +2
The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning…
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography
Ronglin Gong, Jun Wang, Jun Shi
Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised lear…
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Feng Shi, Jun Wang, Jun Shi +6
(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over…
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
Fei Shan, Yaozong Gao, Jun Wang +6
CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported t…