research-article

Open access

Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks

Authors:

Fiona J. Gilbert, and

Mihaela Van Der SchaarAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 3

Article No.: 26, Pages 1 - 24

https://doi.org/10.1145/3453166

Published: 15 July 2021 Publication History

All formats PDF

Abstract

With an aging and growing population, the number of women receiving mammograms is increasing. However, existing techniques for autonomous diagnosis do not surpass a well-trained radiologist. Therefore, to reduce the number of mammograms that require examination by a radiologist, subject to preserving the diagnostic accuracy observed in current clinical practice, we develop Man and Machine Mammography Oracle (MAMMO)—a clinical decision support system capable of determining whether its predicted diagnoses require further radiologist examination. We first introduce a novel multi-view convolutional neural network (CNN) trained using multi-task learning (MTL) to diagnose mammograms and predict the radiological assessments known to be associated with cancer. MTL improves diagnostic performance and triage efficiency while providing an additional layer of model interpretability. Furthermore, we introduce a novel triage network that takes as input the radiological assessment and diagnostic predictions of the multi-view CNN and determines whether the radiologist or CNN will most likely provide the correct diagnosis. Results obtained on a dataset of over 7,000 patients show that MAMMO reduced the number of diagnostic mammograms requiring radiologist reading by 42.8% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone.

References

[1]

Qaisar Abbas. 2016. DeepCAD: A computer-aided diagnosis system for mammographic masses using deep invariant features. Computers 5, 4 (2016).

[2]

Chul Kyun Ahn, Changyong Heo, Heongmin Jin, and Jong Hyo Kim. 2017. A novel deep learning-based approach to high accuracy breast density estimation in digital mammography. Proc. SPIE 10134 (2017), 10134–10134.

[3]

Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharon Alpert, Sharbell Hasoul, Rami Ben-Ari, and Ella Barkan. 2016. A region based convolutional network for tumor detection and classification in breast mammography. In Deep Learning and Data Labeling for Medical Applications, Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, and Julien Cornebise (Eds.). Springer International Publishing, Cham, 197–205.

[4]

Ayelet Akselrod-Ballin, Leonid. Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan, and Ella Barkan. 2017. Deep learning for automatic detection of abnormal findings in breast mammography. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R. S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, and Zhi Lu (Eds.). Springer International Publishing, Cham, 321–329.

[5]

Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 2006. Multi-task feature learning. In Proceedings of the 19th International Conference on Neural Information Processing Systems (NIPS’06). The MIT Press, Cambridge, MA, 41–48.

Digital Library

[6]

Murat Seckin Ayhan and Philipp Berens. 2018. Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In the International Conference on Medical Imaging with Deep Learning.

[7]

A. S. Becker, M. Marcon, S. Ghafoor, M. C. Wurnig, T. Frauenfelder, and A. Boss. 2016. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer.Investig. Radiol. 52 (July 2016), 434–440.

[8]

A. J. Bekker, H. Greenspan, and J. Goldberger. 2016. A multi-view deep learning architecture for classification of breast microcalcifications. In the IEEE 13th International Symposium on Biomedical Imaging (ISBI’16). 726–730.

[9]

Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence D. Jackel, and Urs Muller. 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. CoRR abs/1704.07911 (2017).

[10]

Mireille Broeders, Sue Moss, Lennarth Nyström, Sisse Njor, Håkan Jonsson, Ellen Paap, Nathalie Massat, Stephen Duffy, Elsebeth Lynge, and Eugenio Paci. 2012. The impact of mammographic screening on breast cancer mortality in Europe: A review of observational studies. J. Med. Screen. 19, 1_suppl (2012), 14–25.

[11]

Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley. 2015. Unregistered multiview mammogram analysis with pre-trained deep learning models. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi (Eds.). Springer International Publishing, Cham, 652–660.

[12]

Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley. 2017. Deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions. In Deep Learning for Medical Image Analysis, S. Kevin Zhou, Hayit Greenspan, and Dinggang Shen (Eds.). Academic Press, 321–339.

[13]

François Chollet et al. 2015. Keras. Retrieved from https://keras.io.

[14]

Jesse Davis and Mark Goadrich. 2006. The relationship between precision-recall and ROC curves. In the 23rd International Conference on Machine Learning (ICML’06). ACM, New York, NY, 233–240.

Digital Library

[15]

Stamatia Destounis, Andrea Arieno, Renee Morgan, Christina Roberts, and Ariane Chan. 2017. Qualitative versus quantitative mammographic breast density assessment: Applications for the us and abroad. Diagnostics 7 (05 2017), 30.

[16]

J. Dheeba, N. Albert Singh, and S. Tamil Selvi. 2014. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49 (2014), 45–52.

Digital Library

[17]

Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley. 2014. Deep structured learning for mass segmentation from mammograms. CoRR abs/1410.7454 (2014).

[18]

N. Dhungel, G. Carneiro, and A. P. Bradley. 2015. Automated mass detection in mammograms using cascaded deep learning and random forests. In the International Conference on Digital Image Computing: Techniques and Applications (DICTA’15). 1–8.

[19]

N. Dhungel, G. Carneiro, and A. P. Bradley. 2017. Fully automated classification of mammograms using deep residual neural networks. In the IEEE 14th International Symposium on Biomedical Imaging (ISBI’17). 310–314.

[20]

Stephen W. Duffy, Laszlo Tabár, Hsiu-Hsi Chen, Marit Holmqvist, Ming-Fang Yen, Shahim Abdsalah, Birgitta Epstein, Ewa Frodis, Ljungberg Eva, Christina Hedborg-Melander, Ann Sundbom, Maria Tholin, Mika Wiege, Anders Åkerlund, Hui-Min Wu, Tao-Shin Tung, Yueh-Hsia Chiu, Chen-Pu Chi, Chih-Chung Huang, Robert A. Smith, Måns Rosén, Magnus Stenbeck, and Lars Holmberg. 2002. The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties. Cancer 95, 3 (2002), 458–469.

[21]

N. A. Fonseca, P. Ferreira, I. Dutra, R. Woods, and E. Burnside. 2015. Predicting malignancy from mammography findings and image-guided core biopsies. Int. J. Data Mining Bioinf. 11, 3 (2015), 257–276.

[22]

Krzysztof J. Geras, Stacey Wolfson, S. Gene Kim, Linda Moy, and Kyunghyun Cho. 2017. High-resolution breast cancer screening with multi-view deep convolutional neural networks. CoRR abs/1703.07047 (2017).

[23]

Fiona Gilbert, Lorraine Tucker, Maureen G. C. Gillan, Paula Willsher, Julie Cooke, Karen Duncan, Michael Michell, Hilary Dobson, Yit Yoong Lim, Hema Purushothaman, Celia Strudley, Susan M. Astley, Oliver Morrish, Kenneth Young, and Stephen Duffy. 2015. The TOMMY trial: a comparison of TOMosynthesis with MammographY in the UKNHS Breast Screening Program. Health Technol. Assess. 19 (2015).

[24]

Nima Habibzadeh Motlagh, Mahboobeh Jannesary, HamidReza Aboulkheyr, Pegah Khosravi, Olivier Elemento, Mehdi Totonchi, and Iman Hajirasouliha. 2018. Breast cancer histopathological image classification: A deep learning approach. bioRxiv (2018).

[25]

Michael Heath, Kevin Bower, Richard Moore, and W. Phillip Kegelmeyer. 2001. The digital database for screening mammography. In Proceedings of the 5th International Workshop on Digital Mammography. Medical Physics Publishing, 212–218.

[26]

P. U. Hepsag, S. A. Ozel, and A. Yazici. 2017. Using deep learning for mammography classification. In the International Conference on Computer Science and Engineering (UBMK’17). 418–423.

[27]

Benjamin Q. Huynh, Hui Li, and Maryellen L. Giger. 2016. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imag. 3 (2016).

[28]

M. Jadoon, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt, and Adeel Jadoon. 2017. Three-class mammogram classification based on descriptive CNN features. BioMed Res. Int. 2017, 3640901 (2017).

[29]

Andrew R. Jamieson, Karen Drukker, and Maryellen L. Giger. 2012. Breast image feature learning with adaptive deconvolutional networks. Proc. SPIE 8315 (2012), 8315–8315.

[30]

Fan Jiang, Hui Liu, Shaode Yu, and Yaoqin Xie. 2017. Breast mass lesion classification in mammograms by transfer learning. In Proceedings of the 5th International Conference on Bioinformatics and Computational Biology (ICBCB’17). ACM, New York, NY, 59–62.

Digital Library

[31]

Zhicheng Jiao, Xinbo Gao, Ying Wang, and Jie Li. 2016. A deep feature based framework for breast masses classification. Neurocomputing 197 (2016), 221–231.

Digital Library

[32]

M. Kallenberg, K. Petersen, M. Nielsen, A. Y. Ng, P. Diao, C. Igel, C. M. Vachon, K. Holland, R. R. Winkel, N. Karssemeijer, and M. Lillholm. 2016. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imag. 35, 5 (May 2016), 1322–1331.

[33]

Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in Bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5574–5584. Retrieved from http://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf.

[34]

Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, and Iman Hajirasouliha. 2018. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 27 (2018), 317–328.

[35]

Pavel Kisilev, Eli Sason, Ella Barkan, and Sharbell Hashoul. 2016. Medical image description using multi-task-loss CNN. In Deep Learning and Data Labeling for Medical Applications, Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, and Julien Cornebise (Eds.). Springer International Publishing, Cham, 121–129.

[36]

A. Kolesnikov, X. Zhai, and L. Beyer. 2019. Revisiting self-supervised visual representation learning. In the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19). 1920–1929.

[37]

Thijs Kooi and Nico Karssemeijer. 2017. Classifying symmetrical differences and temporal change in mammography using deep neural networks. CoRR abs/1703.07715 (2017).

[38]

Thijs Kooi, Geert Litjens, Bram van Ginneken, Albert Gubern-Mérida, Clara I. Sánchez, Ritse Mann, Ard den Heeten, and Nico Karssemeijer. 2016. Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35 (2016), 303–312.

[39]

T. Kyono, F. Gilbert, and M. van der Schaar. 2020. Improving workflow efficiency for mammography using machine learning.J. Amer. Coll. Radiol. 17, 1 (2020), 56–63.

[40]

Constance D. Lehman, Robert D. Wellman, Diana S. M. Buist, Karla Kerlikowske, Anna N. A. Tosteson, Diana L. Miglioretti, and Breast Cancer Surveillance Consortium. 2015. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 11 (Nov. 2015), 1828–37.

[41]

Daniel Lévy and Arzav Jain. 2016. Breast mass classification from mammograms using deep convolutional neural networks. In the International Conference on Neural Information Processing Systems (NIPS’16), Vol. abs/1612.00542.

[42]

Changsheng Li, Fan Wei, Junchi Yan, Weishan Dong, Qingshan Liu, and Hongyuan Zha. 2017. Self-paced multi-task learning. In the 31st AAAI Conference on Artificial Intelligence (AAAI’17).

[43]

Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. CoRR abs/1708.02002 (2017).

[44]

Mariëtte Lokate, Rebecca K. Stellato, Wouter B. Veldhuis, Petra H. M. Peeters, and Carla H. van Gils. 2013. Age-related changes in mammographic density and breast cancer risk. Amer. J. Epidemiol. 178, 1 (2013), 101–109.

[45]

Aravindh Mahendran and Andrea Vedaldi. 2015. Visualizing deep convolutional neural networks using natural pre-images. Retrieved from http://arxiv.org/abs/1512.02017.

[46]

Scott McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg Corrado, Ara Darzi, Mozziyar Etemadi, Florencia Garcia-Vicente, Fiona Gilbert, Mark Halling-Brown, Demis Hassabis, Sunny Jansen, Alan Karthikesalingam, Christopher Kelly, Dominic King, and Shravya Shetty. 2020. International evaluation of an AI system for breast cancer screening. Nature 577 (01 2020), 89–94.

[47]

Elliot Meyerson and Risto Miikkulainen. 2018. Beyond shared hierarchies: Deep multitask learning through soft layer ordering. In the International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=BkXmYfbAZ.

[48]

Aly A. Mohamed, Wendie A. Berg, Hong Peng, Yahong Luo, Rachel C. Jankowitz, and Shandong Wu. 2018. A deep learning method for classifying mammographic breast density categories. Med. Phys. 45, 1 (2018), 314–321.

[49]

Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens. 2015. Adding gradient noise improves learning for very deep networks. CoRR abs/1511.06807 (2015).

[50]

Robert M. Nishikawa and Kyongtae T. Bae. 2018. Importance of better human-computer interaction in the era of deep learning: Mammography computer-aided diagnosis as a use case. J. Amer. Coll. Radiol. 15, 1 (Jan. 2018), 49–52.

[51]

Jane Peart, Glen Thomson, and Stephen Wood. 2017. Developing asymmetry in a screening mammogram: A cautionary tale of a missed cancer. J. Med. Imag. Radiat. Oncol. 62, 1 (2017), 77–80.

[52]

Richard Platania, Shayan Shams, Seungwon Yang, Jian Zhang, Kisung Lee, and Seung-Jong Park. 2017. Automated breast cancer diagnosis using deep learning and region of interest detection (BC-DROID). In the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB’17). ACM, New York, NY, 536–543.

[53]

Yuchen Qiu, Yunzhi Wang, Shiju Yan, Maxine Tan, Samuel Cheng, Hong Liu, and Bin Zheng. 2016. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. Proc. SPIE 9785 (2016).

[54]

Dezso Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, and István Csabai. 2017. Detecting and classifying lesions in mammograms with deep learning. CoRR abs/1707.08401 (2017).

[55]

Alejandro Rodriguez-Ruiz, Kristina Lång, Albert Gubern-Mérida, Mireille Broeders, Gisella Gennaro, Paola Clauser, Thomas Helbich, Thomas Mertelmeier, Margarita Chevalier, Matthew Wallis, Ingvar Andersson, Sophia Zackrisson, Ritse Mann, and Ioannis Sechopoulos. 2020. Can AI serve as an independent second reader of mammograms? A simulation study. In Proceedings of the 15th International Workshop on Breast Imaging (IWBI'20).

[56]

Alejandro Rodriguez-Ruiz, Jan-Jurre Mordang, Nico Karssemeijer, Ioannis Sechopoulos, and Ritse M. Mann. 2018. Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support? Proc. SPIE 10718 (2018).

[57]

Santiago Romero-Brufau, Jeanne M. Huddleston, Gabriel J. Escobar, and Mark Liebow. 2015. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit. Care 19, 1 (13 Aug. 2015), 285.

[58]

Ravi Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Mark A. Helvie, Kenny Cha, and Caleb D. Richter. 2017. Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms. In Phys. Med. Biol. 62 (2017).

[59]

Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Jun Wei, and Kenny Cha. 2016. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med. Phys. 43, 12 (2016), 6654–6666.

[60]

Ravi K. Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Kenny Cha, and Mark A. Helvie. 2016. Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis. Proc. SPIE 9785 (2016).

[61]

Thomas Schaffter, Diana S. M. Buist, Christoph I. Lee, Yaroslav Nikulin, Dezső Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, Jiashi Feng, Mengling Feng, Hyo-Eun Kim, Francisco Albiol, Alberto Albiol, Stephen Morrell, Zbigniew Wojna, Mehmet Eren Ahsen, Umar Asif, Antonio Jimeno Yepes, Shivanthan Yohanandan, Simona Rabinovici-Cohen, Darvin Yi, Bruce Hoff, Thomas Yu, Elias Chaibub Neto, Daniel L. Rubin, Peter Lindholm, Laurie R. Margolies, Russell Bailey McBride, Joseph H. Rothstein, Weiva Sieh, Rami Ben-Ari, Stefan Harrer, Andrew Trister, Stephen Friend, Thea Norman, Berkman Sahiner, Fredrik Strand, Justin Guinney, Gustavo Stolovitzky, and the DM DREAM Consortium. 2020. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw. Open 3, 3 (03 2020), e200265–e200265.

[62]

Diane Scutt, Gillian Lancaster, and John Manning. 2006. Breast asymmetry and predisposition to breast cancer. In Breast Canc. Res. 8 (2006), R14.

[63]

Li Shen. 2017. End-to-end training for whole image breast cancer diagnosis using an all convolutional design. CoRR abs/1708.09427 (2017).

[64]

Li Shen, Laurie Margolies, Joseph Rothstein, Eugene Fluder, Russell McBride, and Weiva Sieh. 2019. Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9 (08 2019), 1–12.

[65]

Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015. 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., 2440–2448. Retrieved from http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf.

Digital Library

[66]

S. Suzuki, X. Zhang, N. Homma, K. Ichiji, N. Sugita, Y. Kawasumi, T. Ishibashi, and M. Yoshizawa. 2016. Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In the 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE’16). 1382–1386.

[67]

Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke. 2016. Inception-v4, Inception-ResNet and the impact of residual connections on learning. CoRR abs/1602.07261 (2016).

[68]

Philip M. Tchou, Tamara Miner Haygood, E. Neely Atkinson, Tanya W. Stephens, Paul L. Davis, Elsa M. Arribas, William R. Geiser, and Gary J. Whitman. 2010. Interpretation time of computer-aided detection at screening mammography. Radiology 257, 1 (2010), 40–46.

[69]

Philip Teare, Michael Fishman, Oshra Benzaquen, Eyal Toledano, and Eldad Elnekave. 2017. Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J. Digit. Imag. 30, 4 (2017), 499–505.

[70]

G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, and T. Vercauteren. 2018. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. ArXiv e-prints (July 2018).

[71]

N. Wu, K. J. Geras, Y. Shen, J. Su, S. G. Kim, E. Kim, S. Wolfson, L. Moy, and K. Cho. 2017. Breast density classification with deep convolutional neural networks. ArXiv e-prints (Nov. 2017).

[72]

Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, S. Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, and Krzysztof Geras. 2020. Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms. ArXiv abs/2009.09282 (2020).

[73]

N. Wu, J. Phang, J. Park, Y. Shen, Z. Huang, M. Zorin, S. Jastrzębski, T. Févry, J. Katsnelson, E. Kim, S. Wolfson, U. Parikh, S. Gaddam, L. L. Y. Lin, K. Ho, J. D. Weinstein, B. Reig, Y. Gao, H. Toth, K. Pysarenko, A. Lewin, J. Lee, K. Airola, E. Mema, S. Chung, E. Hwang, N. Samreen, S. G. Kim, L. Heacock, L. Moy, K. Cho, and K. J. Geras. 2020. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imag. 39, 4 (2020), 1184–1194.

[74]

Adam Yala, Constance Lehman, Tal Schuster, Tally Portnoi, and Regina Barzilay. 2019. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292, 1 (2019), 60–66.

[75]

Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, and Constance Lehman. 2019. A deep learning model to triage screening mammograms: A simulation study. Radiology 293, 1 (2019), 38–46.

[76]

Darvin Yi, Rebecca Lynn Sawyer, David Cohn III, Jared Dunnmon, Carson Lam, Xuerong Xiao, and Daniel L. Rubin. 2017. Optimizing and visualizing deep learning for benign/malignant classification in breast tumors. CoRR abs/1705.06362 (May 2017).

[77]

Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas J. Fuchs, and Hod Lipson. 2015. Understanding neural networks through deep visualization. Retrieved from http://arxiv.org/abs/1506.06579.

[78]

Matthew D. Zeiler and Rob Fergus. 2013. Visualizing and understanding convolutional networks. Retrieved from http://arxiv.org/abs/1311.2901.

[79]

Chen Zhang, Jumin Zhao, Jing Niu, and Dengao Li. 2020. New convolutional neural network model for screening and diagnosis of mammograms. PLoS One 15, 8 (08 2020), 1–20.

[80]

Xiaofei Zhang, Yi Zhang, Erik Y. Han, Nathan Jacobs, Qiong Han, Xiaoqin Wang, and Jinze Liu. 2018. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans. NanoBiosci. 17, 3 (2018), 237–242.

[81]

Wentao Zhu and Xiaohui Xie. 2016. Adversarial deep structural networks for mammographic mass segmentation. CoRR abs/1612.05970 (2016).

Cited By

Makino TGeras KCho KKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Generative multitask learning mitigates target-causing confoundingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602918(36546-36558)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602918

Index Terms

Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics

Recommendations

Benign and malignant mammographic image classification based on Convolutional Neural Networks
ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

Computerized breast cancer diagnosis system has played an import role in early cancer diagnosis. For this purpose, we apply deep learning by using convolutional neural networks (CNN) to classify abnormalities, benign or malignant, in mammographic images ...
Read More
Classification Cervical Cancer Using Histology Images
ICCEA '10: Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 01

This papers reports on methodologies and outcome of a study aiming at developing robust tool to evaluate and classify histology images of cervical cancer. Using the histology images acquired from the pathology laboratories in an Indonesian hospital, ...
Read More
Classifying breast lesions in Brazilian thermographic images using convolutional neural networks
Abstract
Breast cancer is the leading cause of death from malignant tumors in women worldwide. Early diagnosis is essential for the treatment and cure of patients. Breast anomalies, such as cysts, cancers and benign tumors, show an increase in blood supply ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 3

Survey Paper

July 2021

226 pages

EISSN:2637-8051

DOI:10.1145/3476113

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2021

Accepted: 01 February 2021

Revised: 01 February 2021

Received: 01 April 2020

Published in HEALTH Volume 2, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
460
Total Downloads

Downloads (Last 12 months)147
Downloads (Last 6 weeks)13

Other Metrics

View Author Metrics

Citations

Cited By

Makino TGeras KCho KKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Generative multitask learning mitigates target-causing confoundingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602918(36546-36558)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602918

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents