Fetching the paper…
Reading the bibliography…
With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing.
J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurement , vol. 20, no. 1, pp. 37–46, 1960
1960
Earlier work this paper cites.
M. Heath, K. Bower, R. Moore, W. P. Kegelmeyer, K. Chang, and S. M. Kumaran, “Current status of the digital database for screening mammography,” in Proceedings of the Fourth International Workshop on Digital Mammography . Kluwer Academic Publishers, 1998, pp. 457–460
1998
Earlier work this paper cites.
N. Karssemeijer, “Automated classification of parenchymal patterns in mammograms,” Physics in Medicine & Biology , vol. 43, no. 2, p. 365, 1998
1998
Earlier work this paper cites.
M. Heath, K. Bower, R. Moore, and W. P. Kegelmeyer, “The digital database for screening mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography . Medical Physics Publishing, 2001, pp. 212–218
2001
Earlier work this paper cites.
D. S. W., T. Laszlo, C. Hsiu-Hsi, H. Marit, Y. Ming-Fang, A. Shahim, E. Birgitta, F. Ewa, L. Eva, H. Christina, S. Ann, T. Maria, W. Mika, Åkerlund Anders, W. Hui-Min, T. Tao-Shin, C. Yueh-Hsia, C. Chen-Pu, H. Chih-Chung, S. R. A., R. Måns, S. Magnus, and H. Lars, “The impact of organized mammography service screening on breast carcinoma mortality in seven swedish counties,” Cancer , vol. 95, no. 3, pp. 458–469, 2002
2002
Earlier work this paper cites.
C. PA, M. DL, Y. BC, and et al, “Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography,” Annals of Internal Medicine , vol. 138, no. 3, pp. 168–175, 2003
2003
Earlier work this paper cites.
D. Scutt, G. Lancaster, and J. Manning, “Breast asymmetry and predisposition to breast cancer,” in Breast cancer research : BCR , vol. 8, 02 2006, p. R14
2006
Earlier work this paper cites.
A. Argyriou, T. Evgeniou, and M. Pontil, “Multi-task feature learning,” in Proceedings of the 19th International Conference on Neural Information Processing Systems , ser. NIPS’06. Cambridge, MA, USA: MIT Press, 2006, pp. 41–48
2006
Earlier work this paper cites.
J. Davis and M. Goadrich, “The relationship between precision-recall and roc curves,” in Proceedings of the 23rd International Conference on Machine Learning , ser. ICML ’06. New York, NY, USA: ACM, 2006, pp. 233–240
2006
Earlier work this paper cites.
M. P. Sesmero, A. I. Ledezma, and A. Sanchis, “Generating ensembles of heterogeneous classifiers using stacked generalization,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , vol. 5, no. 1, pp. 21–34, 2007
2007
Earlier work this paper cites.
G. Heitz, S. Gould, A. Saxena, and D. Koller, “Cascaded classification models: Combining models for holistic scene understanding,” in Advances in Neural Information Processing Systems 21 , ser. NIPS, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., 2009, pp. 641–648
2009
Earlier work this paper cites.
P. M. Tchou, T. M. Haygood, E. N. Atkinson, T. W. Stephens, P. L. Davis, E. M. Arribas, W. R. Geiser, and G. J. Whitman, “Interpretation time of computer-aided detection at screening mammography,” Radiology , vol. 257, no. 1, pp. 40–46, 2010
2010
Earlier work this paper cites.
M. Broeders, S. Moss, L. Nyström, S. Njor, H. Jonsson, E. Paap, N. Massat, S. Duffy, E. Lynge, and E. Paci, “The impact of mammographic screening on breast cancer mortality in europe: A review of observational studies,” Journal of Medical Screening , vol. 19, no. 1_suppl, pp. 14–25, 2012
2012
Earlier work this paper cites.
A. R. Jamieson, K. Drukker, and M. L. Giger, “Breast image feature learning with adaptive deconvolutional networks,” Proc.SPIE , vol. 8315, pp. 8315 – 8315 – 13, 2012
2012
Earlier work this paper cites.
M. Lokate, R. K. Stellato, W. B. Veldhuis, P. H. M. Peeters, and C. H. van Gils, “Age-related changes in mammographic density and breast cancer risk,” American Journal of Epidemiology , vol. 178, no. 1, pp. 101–109, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Dheeba, N. A. Singh, and S. T. Selvi, “Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach,” Journal of Biomedical Informatics , vol. 49, pp. 45 – 52, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. D. Lehman, R. D. Wellman, D. S. M. Buist, K. Kerlikowske, A. N. A. Tosteson, D. L. Miglioretti, and Breast Cancer Surveillance Consortium, “Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.” JAMA internal medicine , vol. 175, no. 11, pp. 1828–37, nov 2015
2015
Earlier work this paper cites.
F. Gilbert, L. Tucker, M. G. Gillan, P. Willsher, J. Cooke, K. Duncan, M. Michell, H. Dobson, Y. Y. Lim, H. Purushothaman, C. Strudley, S. M. Astley, O. Morrish, K. Young, and S. Duffy, “The tommy trial: a comparison of tomosynthesis with mammography in the uknhs breast screening program,” Health Technology Assessment , vol. 19, 2015
2015
Earlier work this paper cites.
G. Carneiro, J. Nascimento, and A. P. Bradley, “Unregistered multiview mammogram analysis with pre-trained deep learning models,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 652–660
2015
Earlier work this paper cites.
S. Sukhbaatar, a. szlam, J. Weston, and R. Fergus, “End-to-end memory networks,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 2440–2448. [Online]. Available: http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf
2015
Earlier work this paper cites.
F. Chollet et al. , “Keras,” https://keras.io , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Romero-Brufau, J. M. Huddleston, G. J. Escobar, and M. Liebow, “Why the c-statistic is not informative to evaluate early warning scores and what metrics to use,” Critical Care , vol. 19, no. 1, p. 285, Aug 2015
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
N. A. Fonseca, P. Ferreira, I. Dutra, R. Woods, and E. Burnside, “Predicting malignancy from mammography findings and image-guided core biopsies,” Int Joural Data Mining Bioinformatics , vol. 11, no. 3, pp. 257–276, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
G. Carneiro, J. Nascimento, and A. P. Bradley, “Chapter 14 - deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions1,” in Deep Learning for Medical Image Analysis , S. K. Zhou, H. Greenspan, and D. Shen, Eds. Academic Press, 2017, pp. 321 – 339
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Teare, M. Fishman, O. Benzaquen, E. Toledano, and E. Elnekave, “Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement,” Journal of Digital Imaging , 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
N. Dhungel, G. Carneiro, and A. P. Bradley, “Automated mass detection in mammograms using cascaded deep learning and random forests,” in 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) , Nov 2015, pp. 1–8
2015
Cited alongside, same era.
A. Akselrod-Ballin, L. Karlinsky, S. Alpert, S. Hasoul, R. Ben-Ari, and E. Barkan, “A region based convolutional network for tumor detection and classification in breast mammography,” in Deep Learning and Data Labeling for Medical Applications , G. Carneiro, D. Mateus, L. Peter, A. Bradley, J. M. R. S. Tavares, V. Belagiannis, J. P. Papa, J. C. Nascimento, M. Loog, Z. Lu, J. S. Cardoso, and J. Cornebise, Eds. Cham: Springer International Publishing, 2016, pp. 197–205
2016
Cited alongside, same era.
B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” Journal of Medical Imaging , vol. 3, pp. 3 – 3 – 5, 2016
2016
Cited alongside, same era.
Y. Qiu, Y. Wang, S. Yan, M. Tan, S. Cheng, H. Liu, and B. Zheng, “An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology,” Proc.SPIE , vol. 9785, pp. 9785 – 9785 – 6, 2016
2016
Cited alongside, same era.
R. K. Samala, H.-P. Chan, L. M. Hadjiiski, K. Cha, and M. A. Helvie, “Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis,” Proc.SPIE , vol. 9785, pp. 9785 – 9785 – 7, 2016
2016
Cited alongside, same era.
Q. Abbas, “Deepcad: A computer-aided diagnosis system for mammographic masses using deep invariant features,” Computers , vol. 5, no. 4, 2016
2016
Cited alongside, same era.
Z. Jiao, X. Gao, Y. Wang, and J. Li, “A deep feature based framework for breast masses classification,” Neurocomputing , vol. 197, pp. 221 – 231, 2016
2016
Cited alongside, same era.
2017
Later among the works it cites.
N. Wu, K. J. Geras, Y. Shen, J. Su, S. G. Kim, E. Kim, S. Wolfson, L. Moy, and K. Cho, “Breast density classification with deep convolutional neural networks,” ArXiv e-prints , Nov. 2017
2017
Later among the works it cites.
J. Peart, G. Thomson, and S. Wood, “Developing asymmetry in a screening mammogram: A cautionary tale of a missed cancer,” Journal of Medical Imaging and Radiation Oncology , vol. 62, no. 1, pp. 77–80, 2017
2017
Later among the works it cites.
C. K. Ahn, C. Heo, H. Jin, and J. H. Kim, “A novel deep learning-based approach to high accuracy breast density estimation in digital mammography,” Proc.SPIE , vol. 10134, pp. 10 134 – 10 134 – 7, 2017
2017
Later among the works it cites.
N. Dhungel, G. Carneiro, and A. P. Bradley, “Fully automated classification of mammograms using deep residual neural networks,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) , April 2017, pp. 310–314
2017
Later among the works it cites.
R. Platania, S. Shams, S. Yang, J. Zhang, K. Lee, and S.-J. Park, “Automated breast cancer diagnosis using deep learning and region of interest detection (bc-droid),” in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics , ser. ACM-BCB ’17. New York, NY, USA: ACM, 2017, pp. 536–543
2017
Later among the works it cites.
M. Jadoon, Q. Zhang, I. Ul Haq, S. Butt, and A. Jadoon, “Three-class mammogram classification based on descriptive cnn features,” BioMed Research International , vol. 2017, no. 3640901, 2017
2017
Later among the works it cites.
P. U. Hepsag, S. A. Ozel, and A. Yazici, “Using deep learning for mammography classification,” in 2017 International Conference on Computer Science and Engineering (UBMK) , Oct 2017, pp. 418–423
2017
Later among the works it cites.
Y. Nikulin. (2017) Dm challenge therapixel submission. [Online]. Available: https://www.synapse.org/#!Synapse:syn9773040/wiki/426908
2017
Later among the works it cites.
R. Samala, H.-P. Chan, L. M Hadjiiski, M. A Helvie, K. Cha, and C. D Richter, “Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms,” in Physics in Medicine and Biology , vol. 62, 10 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
F. Jiang, H. Liu, S. Yu, and Y. Xie, “Breast mass lesion classification in mammograms by transfer learning,” in Proceedings of the 5th International Conference on Bioinformatics and Computational Biology , ser. ICBCB ’17. New York, NY, USA: ACM, january 2017, pp. 59–62
2017
Later among the works it cites.
2017
Later among the works it cites.
A. A. Mohamed, W. A. Berg, H. Peng, Y. Luo, R. C. Jankowitz, and S. Wu, “A deep learning method for classifying mammographic breast density categories,” Medical Physics , vol. 45, no. 1, pp. 314–321, 2018
2018
Closest in time.
N. K. I. S. R. M. M. Alejandro Rodriguez-Ruiz, Jan-Jurre Mordang, “Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?” Proc.SPIE , vol. 10718, pp. 10 718 – 10 718 – 10, 2018. [Online]. Available: https://doi.org/10.1117/12.2317937
2018
Closest in time.
R. M. Nishikawa and K. T. Bae, “Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis asaUse Case,” Journal of the American College of Radiology , vol. 15, no. 1, pp. 49–52, jan 2018
2018
Closest in time.
G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, and T. Vercauteren, “Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,” ArXiv e-prints , Jul. 2018
2018
Closest in time.
M. S. Ayhan and P. Berens, “Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks,” in International conference on Medical Imaging with Deep Learning , 2018
2018
Closest in time.
X. Zhang, Y. Zhang, E. Y. Han, N. Jacobs, Q. Han, X. Wang, and J. Liu, “Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks,” IEEE Transactions on NanoBioscience , pp. 1–1, 2018
2018
Closest in time.
T. de Moor, A. Rodriguez-Ruiz, R. Mann, and J. Teuwen, “Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network,” ArXiv e-prints , Feb. 2018
2018
Closest in time.
P. Khosravi, E. Kazemi, M. Imielinski, O. Elemento, and I. Hajirasouliha, “Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images,” EBioMedicine , vol. 27, pp. 317 – 328, 2018
2018
Closest in time.
N. Habibzadeh Motlagh, M. Jannesary, H. Aboulkheyr, P. Khosravi, O. Elemento, M. Totonchi, and I. Hajirasouliha, “Breast cancer histopathological image classification: A deep learning approach,” bioRxiv , 2018
2018
Closest in time.