5 citations · 13 across the 6 of their papers we have counts for
6 papers
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network
Jialun Wu, Yang Liu, Zeyu Gao +3
Constructing large-scaled medical knowledge graphs can significantly boost healthcare applications for medical surveillance, bring much attention from recent research. An essential…
A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data
Jialun Wu, Zeyu Gao, Haichuan Zhang +4
Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-to-day routine. This lead to the current generalized standards tha…
W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image
Anyu Mao, Jialun Wu, Xinrui Bao +3
Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, and counting are particularly pr…
A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning
Jialun Wu, Haichuan Zhang, Zeyu Gao +4
Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treat…
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Zeyu Gao, Bangyang Hong, Xianli Zhang +6
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the p…
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
Zeyu Gao, Jiangbo Shi, Xianli Zhang +6
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei gr…