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Fully automated classification of mammograms using deep residual neural networks

Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign.  ...  We empirically show on the publicly available INbreast dataset, that the proposed mResNet classifies mammograms into malignant or normal/benign with an AUC of 0.8.  ...  The aim of this paper is to present a novel approach for the fully automated classification of mammograms using deep residual neural networks [14] .  ... 
doi:10.1109/isbi.2017.7950526 dblp:conf/isbi/DhungelCB17 fatcat:kmq5yr6bu5ejjorchz7z4f5sja

An Effective Two Way Classification of Breast Cancer Images: A Detailed Review

Sinthia P, Malathi M
2018 Asian Pacific Journal of Cancer Prevention  
Second type algorithm is Support Vector Machine (SVM), which is used to identify the most suitable function which differentiates the members based on the training data.  ...  Two-way mode data mining algorithms are used due to thinly dispersed distribution of abnormal mammograms.  ...  Region based edge-profile Acutance measures (Multi-class classification) 92 Verma et al., (2005) Statistical features Fuzzy Neural Network (Multi-class classification) 83 Wei et al., (2001) Statistical  ... 
doi:10.31557/apjcp.2018.19.12.3335 fatcat:gsywptptsvhn5opsdcvrtlcrqy

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  method to improve CAD performance of classifying breast lesions [10575-92] 10575 2M Recurrent neural networks for breast lesion classification based on DCE-MRIs [10575-93] 10575 2N Multi-resolution  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification [article]

Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
2016 arXiv   pre-print
networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.  ...  We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.  ...  In addition, multi-stage training cannot fully explore the power of the deep network. Thus, an end-to-end approach for whole mammogram classification is preferred for this problem.  ... 
arXiv:1612.05968v1 fatcat:q4m6knjzjbbubefy5piadjc3qq

Image Classification Using Generalized Multiscale RBF Networks and Discrete Cosine Transform

Carlos Beltran Perez, Hua-Liang Wei
2018 2018 24th International Conference on Automation and Computing (ICAC)  
Based on the new methodology a novel computer aided diagnosis (CAD) system for cancer detection in X-ray mammograms was designed.  ...  Classification results show that the new CAD method helped reach a competitive diagnostic accuracy of 93.5%.  ...  METHODOLOGY The DCT MSRBF feature value extraction method is mainly based on three algorithms: RBF neural network in the multi-scale version, the FROLS algorithm and the Discrete Cosine Transform.  ... 
doi:10.23919/iconac.2018.8748965 dblp:conf/iconac/PerezW18 fatcat:5uz4re3utjgqtfwynxmrqlpz44

Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network

Bhanu Prakash Sharma, Ravindra Kumar Purwar
2022 Advances in Distributed Computing and Artificial Intelligence Journal  
Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector.  ...  Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.  ...  Authors would like to thank to Government of India for Visveswaraya Fellowship scheme for research under which this work has been carried out.  ... 
doi:10.14201/adcaij2021104419434 fatcat:6i34bspmhrc7jmlamv2542pv44

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification [chapter]

Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
2017 Lecture Notes in Computer Science  
networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.  ...  We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.  ...  In addition, multi-stage training cannot fully explore the power of the deep network. Thus, an end-to-end approach for whole mammogram classification is preferred for this problem.  ... 
doi:10.1007/978-3-319-66179-7_69 fatcat:e4ajd7d3o5ewrcysvkypwi7gva

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification [article]

Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
2016 bioRxiv   pre-print
networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.  ...  We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.  ...  In addition, multi-stage training cannot fully explore the power of the deep network. Thus, an end-to-end approach for whole mammogram classification is preferred for this problem.  ... 
doi:10.1101/095794 fatcat:l4dudtnyrrcftfadqct22fuz6u

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification [article]

Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
2017 arXiv   pre-print
networks for mass classification based on whole mammogram without the aforementioned ROIs.  ...  We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.  ...  The proposed deep multi-instance networks are shown to provide robust performance for whole mammogram classification on the INbreast dataset [14] .  ... 
arXiv:1705.08550v1 fatcat:t3eyw6ccf5h6vekpcx4wlt5cea

Front Matter: Volume 10718

Elizabeth A. Krupinski
2018 14th International Workshop on Breast Imaging (IWBI 2018)  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume using a Base  ...  neural network as observers [10718-68] INTERACTIVE POSTER SESSION 10718 11 A deep learning framework for micro-calcification detection in 2D mammography and C-view [10718-29] Multi-scale morphological  ... 
doi:10.1117/12.2502754 dblp:conf/iwbi/X18 fatcat:pwftmdgjcza3lnhzk4yua6npiy

Artificial Intelligence For Breast Cancer Detection: Trends Directions [article]

Shahid Munir Shah, Rizwan Ahmed Khan, Sheeraz Arif, Unaiza Sajid
2021 arXiv   pre-print
Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based  ...  Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets.  ...  It is a method through which a large scale population is screened for breast cancer detection by analyzing their mammograms.  ... 
arXiv:2110.00942v1 fatcat:ufatvglhjzdrjcositsa45oy7e

Multi-View Feature Fusion based Four Views Model for Mammogram Classification using Convolutional Neural Network

Hasan Nasir Khan, Ahmad Raza Shahid, Basit Raza, Amir Hanif Dar, Hani Alquhayz
2019 IEEE Access  
Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction.  ...  In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram.  ...  PROPOSED FOUR-VIEW FEATURES FUSION BASED CADx SYSTEM It is a deep convolutional neural network model that has been used for training on four mammogram views of each patient separately.  ... 
doi:10.1109/access.2019.2953318 fatcat:455h4vtzdjg53gydbuebcsywtm

Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention [article]

Yutong Yan, Pierre-Henri Conze, Gwenolé Quellec, Mathieu Lamard, Béatrice Cochener, Gouenou Coatrieux
2020 arXiv   pre-print
First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization.  ...  In this work, we present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.  ...  The former is based on a deep detection model extended based on a novel multi-scale fusion procedure to reduce wrong proposals and further improve detection accuracy (Sect.3.2).  ... 
arXiv:2002.12079v2 fatcat:4vrk7ap3fffftf72666mtvvloa

INTELLIGENT DETECTION AND CLASSIFICATION OF MICROCALCIFICATION IN COMPRESSED MAMMOGRAM IMAGE

Benjamin Joseph, Baskaran Ramachandran, Priyadharshini Muthukrishnan
2015 Image Analysis and Stereology  
Moreover it gives good classification responses for compressed mammogram image.  ...  This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way.  ...  In this work, Classification of micro-calcification as benign or malignant is done based on multi-wavelet features using PNN (Probabilistic Neural Network).  ... 
doi:10.5566/ias.1290 fatcat:p677ril4zfhl3ikpzydobrp43u

Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis

Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar
2019 Machine Learning in Health Care  
The recent promise of machine learning on medical images have led to an influx of studies using deep learning for autonomous mammogram diagnosis.  ...  We show on full-field mammograms that multi-task learning has three advantages: 1) learning refined feature representations associated with cancer improves the classification performance of the diagnosis  ...  Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Transactions on NanoBioscience, pages 1-1, 2018.  ... 
dblp:conf/mlhc/KyonoGS19 fatcat:cjnbjuiarfabznyzf6pjg22yha
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