Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels
HER2 overexpression is a prognostic and predictive factor observed in about 15% to 20% of breast cancer cases. The assessment of its expression directly affects the selection of treatment and prognosis. The measurement of HER2 status is performed by an expert pathologist who assigns a score of 0, 1,...
Main Authors: | Anibal Pedraza, Lucia Gonzalez, Oscar Deniz, Gloria Bueno |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/17/3/97 |
Similar Items
-
Singular Nuclei Segmentation for Automatic <i>HER2</i> Quantification Using CISH Whole Slide Images
by: Md Shakhawat Hossain, et al.
Published: (2022-09-01) -
Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning
by: Yuxuan Che, et al.
Published: (2023-01-01) -
Ultrasound findings in prediction of breast cancer histological grade and HER2 status
by: Khaleel I. Mohson
Published: (2016-04-01) -
Digital Validation in Breast Cancer Needle Biopsies: Comparison of Histological Grade and Biomarker Expression Assessment Using Conventional Light Microscopy, Whole Slide Imaging, and Digital Image Analysis
by: Ji Eun Choi, et al.
Published: (2024-03-01) -
Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade
by: Sonal Kothari, et al.
Published: (2013-01-01)