Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The importance of stain normalization in colorectal tissue classification with convolutional networks
192
Zitationen
9
Autoren
2017
Jahr
Abstract
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.972 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.785 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.508 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.111 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.417 Zit.