MGBN: Convolutional neural networks for automated benign and malignant breast masses classification
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
MGBN: Convolutional neural networks for automated benign and malignant breast masses classification
Authors
Keywords
-
Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-05-09
DOI
10.1007/s11042-021-10929-6
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Automated Machine Learning: Review of the State-of-the-Art and Opportunities for Healthcare
- (2020) Jonathan Waring et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Deep learning in generating radiology reports: A survey
- (2020) Maram Mahmoud A. Monshi et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma
- (2020) Yu Fu et al. MEDICAL PHYSICS
- An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm
- (2020) Chiranji Lal Chowdhary et al. SENSORS
- Morph_SPCNN model and its application in breast density segmentation
- (2020) Yunliang Qi et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
- (2019) Adam Yala et al. RADIOLOGY
- Multi-level nested pyramid network for mass segmentation in mammograms
- (2019) Runze Wang et al. NEUROCOMPUTING
- Automated classification of histopathology images using transfer learning
- (2019) Muhammed Talo ARTIFICIAL INTELLIGENCE IN MEDICINE
- Generative adversarial network in medical imaging: A review
- (2019) Xin Yi et al. MEDICAL IMAGE ANALYSIS
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases
- (2019) Morteza Heidari et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review
- (2018) Emilie L Henriksen et al. ACTA RADIOLOGICA
- Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review
- (2018) Nisreen I.R. Yassin et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Breast mass classification via deeply integrating the contextual information from multi-view data
- (2018) Hongyu Wang et al. PATTERN RECOGNITION
- Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey
- (2018) Qinghua Huang et al. Biomed Research International
- Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
- (2018) Alejandro Rodríguez-Ruiz et al. RADIOLOGY
- Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning
- (2017) Gustavo Carneiro et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- A survey on deep learning in medical image analysis
- (2017) Geert Litjens et al. MEDICAL IMAGE ANALYSIS
- National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium
- (2017) Constance D. Lehman et al. RADIOLOGY
- A curated mammography data set for use in computer-aided detection and diagnosis research
- (2017) Rebecca Sawyer Lee et al. Scientific Data
- Breast mass classification in digital mammography based on extreme learning machine
- (2016) Weiying Xie et al. NEUROCOMPUTING
- Benign and malignant breast tumors classification based on region growing and CNN segmentation
- (2015) Rahimeh Rouhi et al. EXPERT SYSTEMS WITH APPLICATIONS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer
- (2013) Maryellen L. Giger et al. Annual Review of Biomedical Engineering
- INbreast
- (2011) Inês C. Moreira et al. ACADEMIC RADIOLOGY
- Mammogram retrieval on similar mass lesions
- (2010) Chia-Hung Wei et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- A review of automatic mass detection and segmentation in mammographic images
- (2009) Arnau Oliver et al. MEDICAL IMAGE ANALYSIS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now