An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features
This work addresses diagnostic accuracy issues for breast cancer patients by providing an automated HER-2 scoring method, though it appears incremental as it builds on existing color feature techniques without major methodological shifts.
The paper tackled the problem of inter-pathologist variability in HER-2 scoring for breast cancer diagnosis by proposing an automatic patch-based algorithm using color features, achieving over 90% concordance with a pathologist in experiments.
Breast cancer (BC) is the most common cancer among women world-wide, 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.