FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening
Authors:
- 1 October 2021
- journal article
- research article
- Published by Elsevier in Computers in Biology and Medicine
- Vol. 137, 104800
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61961037, 62061023)
- Natural Science Foundation of Gansu Province (18JR3RA288)
This publication has 21 references indexed in Scilit:
- Automated Breast Ultrasound Lesions Detection Using Convolutional Neural NetworksIEEE Journal of Biomedical and Health Informatics, 2017
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Fully Convolutional Networks for Semantic SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVMJournal of Digital Imaging, 2014
- Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of CancerAnnual Review of Biomedical Engineering, 2013
- Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoringComputers in Biology and Medicine, 2013
- A Novel Approach in Malignancy Detection of Computer Aided DiagnosisAmerican Journal of Applied Sciences, 2012
- INbreastAcademic Radiology, 2012
- An Interactive System for Computer-Aided Diagnosis of Breast MassesJournal of Digital Imaging, 2012