The aim of the study is to evaluate the usefulness of high-tech endoscopy in clinical practice. The important elements of this evaluation are: * Evaluation of the character of neoplastic lesions in th

NIAID Data Ecosystem50

S1 Fig. Evaluation procedure and dataset division for tumor and MSI classifier models. S2 Fig. Tumor tissue probability and distribution per slide. S3 Fig. Datasets for train and test for the MSI classifier. S4 Fig. Comparing performances between the corresponding and cross tissue trained models. S5 Fig. Comparing performances between single-tissue and multi-tissue trained models. S6 Fig. Comparing performances between two-tissue and three-tissue trained models. S1 Table. Detailed model structure. S2 Table. Performance metrics.

肿瘤分类

微卫星不稳定性分析

NIAID Data Ecosystem30

Untitled Item

病理学

肿瘤分类

The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor ty

DataCite Commons2023-06-30 更新90

Final Tumor Classifier

肿瘤分类

机器学习

Tumor classifier state dict

DataCite Commons2021-02-07 更新40