Convolutional neural network for automated mass segmentation in mammography
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Title
Convolutional neural network for automated mass segmentation in mammography
Authors
Keywords
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Journal
BMC BIOINFORMATICS
Volume 21, Issue S1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-12-09
DOI
10.1186/s12859-020-3521-y
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Note: Only part of the references are listed.- Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles
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- (2018) Mohammed A. Al-masni et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
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- (2018) Mugahed A. Al-antari et al. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
- Deep learning for image-based cancer detection and diagnosis − A survey
- (2018) Zilong Hu et al. PATTERN RECOGNITION
- Detecting and classifying lesions in mammograms with Deep Learning
- (2018) Dezső Ribli et al. Scientific Reports
- Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network
- (2018) Hwejin Jung et al. PLoS One
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A deep learning approach for the analysis of masses in mammograms with minimal user intervention
- (2017) Neeraj Dhungel et al. MEDICAL IMAGE ANALYSIS
- Large scale deep learning for computer aided detection of mammographic lesions
- (2017) Thijs Kooi et al. MEDICAL IMAGE ANALYSIS
- A survey on deep learning in medical image analysis
- (2017) Geert Litjens et al. MEDICAL IMAGE ANALYSIS
- Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
- (2014) Jacques Ferlay et al. INTERNATIONAL JOURNAL OF CANCER
- Is full-field digital mammography more accurate than screen-film mammography in overall population screening? A systematic review and meta-analysis
- (2013) Fabiano H. Souza et al. BREAST
- Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method
- (2013) Xiaoming Liu et al. IEEE Systems Journal
- Selective Search for Object Recognition
- (2013) J. R. R. Uijlings et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
- (2013) Kenneth Clark et al. JOURNAL OF DIGITAL IMAGING
- INbreast
- (2011) Inês C. Moreira et al. ACADEMIC RADIOLOGY
- Mass segmentation using a combined method for cancer detection
- (2011) Jun Liu et al. BMC Systems Biology
- Breast Mass Segmentation in Mammography Using Plane Fitting and Dynamic Programming
- (2009) Enmin Song et al. ACADEMIC RADIOLOGY
- Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances
- (2009) Jinshan Tang et al. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
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