An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features
An illustration of our method
Abstract
Breast cancer (BC) is the most common cancer among women worldwide, approximately 20-25% of BCs are HER-2 positive. Analysis of HER-2 is fundamental to defining the appropriate therapy for patients with breast cancer. Inter-pathologist variability in the test results can affect diagnostic accuracy. The present study intends to propose an automatic scoring HER-2 algorithm. Based on color features, the technique is fully-automated and avoids segmentation, showing a concordance higher than 90% with a pathologist in the experiments realized.
Type
Publication
In 2018 Brazilian Symposium of Applied Computing in Health
Jeovane Honório Alves
Machine Learning Researcher
My research interests include neural architecture search, computer vision and medical image processing.