ARF-Net: An Adaptive Receptive Field Network for breast mass segmentation in whole mammograms and ultrasound images
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
ARF-Net: An Adaptive Receptive Field Network for breast mass segmentation in whole mammograms and ultrasound images
Authors
Keywords
Segmentation, Mammogram, Ultrasound image, Deep learning, Encoder-decoder
Journal
Biomedical Signal Processing and Control
Volume 71, Issue -, Pages 103178
Publisher
Elsevier BV
Online
2021-09-18
DOI
10.1016/j.bspc.2021.103178
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multiscale fused network with additive channel–spatial attention for image segmentation
- (2021) Chengling Gao et al. KNOWLEDGE-BASED SYSTEMS
- MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation
- (2021) Run Su et al. Frontiers in Genetics
- Mammographic mass segmentation using multichannel and multiscale fully convolutional networks
- (2020) Shengzhou Xu et al. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
- (2020) Michal Byra et al. Biomedical Signal Processing and Control
- Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework
- (2020) Vivek Kumar Singh et al. EXPERT SYSTEMS WITH APPLICATIONS
- Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
- (2019) Andrik Rampun et al. MEDICAL IMAGE ANALYSIS
- AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
- (2019) Hui Sun et al. PHYSICS IN MEDICINE AND BIOLOGY
- Multiscale receptive field based on residual network for pancreas segmentation in CT images
- (2019) Feiyan Li et al. Biomedical Signal Processing and Control
- Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
- (2018) Mohammed A. Al-masni et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- (2018) Liang-Chieh Chen et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
- (2018) Moi Hoon Yap et al. IEEE Journal of Biomedical and Health Informatics
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A deep learning method for classifying mammographic breast density categories
- (2017) Aly A. Mohamed et al. MEDICAL PHYSICS
- A curated mammography data set for use in computer-aided detection and diagnosis research
- (2017) Rebecca Sawyer Lee et al. Scientific Data
- Breast Ultrasound Image Classification Based on Multiple-Instance Learning
- (2012) Jianrui Ding et al. JOURNAL OF DIGITAL IMAGING
- Automatic mass segmentation on mammograms combining random walks and active contour
- (2012) Xin Hao et al. Journal of Zhejiang University-SCIENCE C-Computers & Electronics
- Mammography segmentation with maximum likelihood active contours
- (2012) Peyman Rahmati et al. MEDICAL IMAGE ANALYSIS
- INbreast
- (2011) Inês C. Moreira et al. ACADEMIC RADIOLOGY
- Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images
- (2011) Woo Kyung Moon et al. ULTRASOUND IN MEDICINE AND BIOLOGY
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started