Search Results for author:
Found 9 papers, 2 papers with code
Meta Mask Correction for Nuclei Segmentation in Histopathological Image
However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.
PIMIP: An Open Source Platform for Pathology Information Management and Integration
However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information.
A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning
Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs).
A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data
In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network
To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports.
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor.
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading.
OpenHI2 -- Open source histopathological image platform
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine.
Effects of annotation granularity in deep learning models for histopathological images
In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset.
