HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

H&E染色 计算机科学 乳腺癌 数字化病理学 人工智能 排名(信息检索) 机器学习 病理 医学
作者
Eduardo Conde-Sousa,João Vale,Ming Feng,Kele Xu,Yin Wang,Vincenzo Della Mea,David La Barbera,Ehsan Montahaei,Mahdieh Baghshah,Andreas Turzynski,Jacob Gildenblat,Eldad Klaiman,Yiyu Hong,Guilherme Aresta,Teresa Araújo,Paulo Aguiar,Catarina Eloy,António Polónia
出处
期刊:Journal of Imaging [MDPI AG]
卷期号:8 (8): 213-213
标识
DOI:10.3390/jimaging8080213
摘要

Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art.
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