Using Convolutional Neural Networks with Direct Acyclic Graph Architecture in Segmentation of Breast Lesions in US Images
- 1 October 2019
- conference paper
- conference paper
- Published by Springer Science and Business Media LLC in IFMBE Proceedings (IFMBE)
Abstract
No abstract availableThis publication has 11 references indexed in Scilit:
- Automated Breast Ultrasound Lesions Detection Using Convolutional Neural NetworksIEEE Journal of Biomedical and Health Informatics, 2017
- A novel deep learning-based approach to high accuracy breast density estimation in digital mammographyPublished by SPIE-Intl Soc Optical Eng ,2017
- Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural NetworksPublished by Springer Science and Business Media LLC ,2016
- Accurate and fully automatic segmentation of breast ultrasound images by combining image boundary and region informationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Deep Learning and Structured Prediction for the Segmentation of Mass in MammogramsPublished by Springer Science and Business Media LLC ,2015
- Ultrasound image segmentation by using a FIR neural networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Advances in oncologic imagingCA: A Cancer Journal for Clinicians, 2012
- Learning-based automatic breast tumor detection and segmentation in ultrasound imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Solid breast nodules: use of sonography to distinguish between benign and malignant lesions.Radiology, 1995
- Directed GraphsPublished by Wiley ,1992