5 citations · 15 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images
Zeyu Gao, Pargorn Puttapirat, Jiangbo Shi +1
Obtaining a large amount of labeled data in medical imaging is laborious and time-consuming, especially for histopathology. However, it is much easier and cheaper to get unlabeled…
Effects of annotation granularity in deep learning models for histopathological images
Jiangbo Shi, Zeyu Gao, Haichuan Zhang +4
Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machi…