IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology
Authors
Keywords
Immunohistochemistry of breast cancer, Whole slide images, Automated Allred score, Automated cell nuclei segmentation, Fully convolutional neural network
Journal
APPLIED SOFT COMPUTING
Volume -, Issue -, Pages 107136
Publisher
Elsevier BV
Online
2021-01-23
DOI
10.1016/j.asoc.2021.107136
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images
- (2020) Elima Hussain et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation
- (2018) Monjoy Saha et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks
- (2018) Dalal Bardou et al. IEEE Access
- Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images
- (2018) Aymen Mouelhi et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- AutoIHC-scoring : a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue
- (2017) S. TEWARY et al. JOURNAL OF MICROSCOPY
- MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images
- (2017) T. MUNGLE et al. JOURNAL OF MICROSCOPY
- A survey on deep learning in medical image analysis
- (2017) Geert Litjens et al. MEDICAL IMAGE ANALYSIS
- Exploiting ensemble learning for automatic cataract detection and grading
- (2016) Ji-Jiang Yang et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- A novel ensemble method for classifying imbalanced data
- (2015) Zhongbin Sun et al. PATTERN RECOGNITION
- Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
- (2015) Fan Hu et al. Remote Sensing
- Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method
- (2013) Aymen Mouelhi et al. Biomedical Signal Processing and Control
- A new automatic image analysis method for assessing estrogen receptors’ status in breast tissue specimens
- (2013) Aymen Mouelhi et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms
- (2011) Akin Ozcift et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Automated segmentation of tissue images for computerized IHC analysis
- (2010) S. Di Cataldo et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- A systematic analysis of performance measures for classification tasks
- (2009) Marina Sokolova et al. INFORMATION PROCESSING & MANAGEMENT
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation