相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。A context-sensitive deep learning approach for microcalcification detection in mammograms
Juan Wang et al.
PATTERN RECOGNITION (2018)
Detecting and classifying lesions in mammograms with Deep Learning
Dezso Ribli et al.
SCIENTIFIC REPORTS (2018)
Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network
Hwejin Jung et al.
PLOS ONE (2018)
Alzheimer's Disease Variants with the Genome-Wide Significance are Significantly Enriched in Immune Pathways and Active in Immune Cells
Qinghua Jiang et al.
MOLECULAR NEUROBIOLOGY (2017)
Deep Learning in Mammography Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer
Anton S. Becker et al.
INVESTIGATIVE RADIOLOGY (2017)
Multiple sclerosis risk pathways differ in Caucasian and Chinese populations
Guiyou Li et al.
JOURNAL OF NEUROIMMUNOLOGY (2017)
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
Neeraj Dhungel et al.
MEDICAL IMAGE ANALYSIS (2017)
Large scale deep learning for computer aided detection of mammographic lesions
Thijs Kooi et al.
MEDICAL IMAGE ANALYSIS (2017)
Grading of invasive breast carcinoma through Grassmannian VLAD encoding
Kosmas Dimitropoulos et al.
PLOS ONE (2017)
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
Zhongyi Han et al.
SCIENTIFIC REPORTS (2017)
Representation learning for mammography mass lesion classification with convolutional neural networks
John Arevalo et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)
Fully automatic face normalization and single sample face recognition in unconstrained environments
Mohammad Haghighat et al.
EXPERT SYSTEMS WITH APPLICATIONS (2016)
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring
Michiel Kallenberg et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
A deep feature based framework for breast masses classification
Zhicheng Jiao et al.
NEUROCOMPUTING (2016)
Radiomics: Images Are More than Pictures, They Are Data
Robert J. Gillies et al.
RADIOLOGY (2016)
Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning
Jinhua Wang et al.
SCIENTIFIC REPORTS (2016)
INbreast: Toward a Full-field Digital Mammographic Database
Ines C. Moreira et al.
ACADEMIC RADIOLOGY (2012)
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Jinchang Ren
KNOWLEDGE-BASED SYSTEMS (2012)
A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms
Deepa Sankar et al.
JOURNAL OF DIGITAL IMAGING (2010)
Computer-aided detection and classification of microcalcifications in mammograms: a survey
HD Cheng et al.
PATTERN RECOGNITION (2003)
Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening
L Tabar et al.
LANCET (2003)
An SVM classifier to separate false signals from microcalcifications in digital mammograms
A Bazzani et al.
PHYSICS IN MEDICINE AND BIOLOGY (2001)
分享论文
