However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.
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.
Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs).
In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.
To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports.
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor.
In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading.
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine.
In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset.