DOCSLIB.ORG

Computational Intelligence in Patient-Sensitive Medical

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Computational Intelligence in Patient-Sensitive Medical Computational Intelligence in Patient-Sensitive Medical Decision Systems Report from Walter Karplus Summer Research Grant awarded by IEEE Computational Intelligence Society Summer 2007 Maciej A. Mazurowski1 Academic Advisors: Jacek M. Zurada1 and Georgia D. Tourassi2 1 Computational Intelligence Laboratory Electrical and Computer Engineering Department University of Louisville 2 Duke Advanced Imaging Laboratories Duke University Medical Center Abstract This report summarizes the work sponsored by IEEE Walter Karplus Summer Research grant awarded in 2007 by IEEE Computational Intelligence Society. Within this project a cooperative re- search was performed at the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center. In the project, en- semble techniques were applied to improve performance of a previously presented computer-aided detection (CAD) classifier. The classifier was designed for detection of masses in mammograms. As a result of the project, a paper was written and presented at SPIE Medical Imaging 2008. 1 Introduction and motivations Computer-aided decision (CAD) systems have gained interest and popularity in medical diagnosis. CAD systems provide a second opinion regarding patients diagnosis based on previously acquired clinical information. Among other tasks, CAD systems have been thus far successfully applied in breast, lung and colon cancer detection [1, 2, 3, 4, 5]. Some of the presented systems utilize computational intelligence techniques [6, 7, 8, 9, 10] Recently, some efforts have been made to incorporate ensemble techniques to CAD systems [11, 12, 13, 14, 15, 16]. In this project, we have made further investigations on this topic. In our studies we used a modular approach where the cases were initially divided into separate subgroups. Then, classifiers based on each of these groups served as experts in the classification task. After several sub-classifiers were constructed, a combiner was trained to fuse the particular decisions in one final response. 1 2 Multidisciplinary nature of the project The implemented project had a highly multidisciplinary character. It involved knowledge from the fields of computational and artificial intelligence, database management and medical diagnosis. Following this requirement, the project involved the collaboration of scientists from different domains. Maciej Mazurowski and Dr. Jacek Zurada (Computational Intelligence Laboratory, University of Louisville) were responsible for the development and application of computational intelligence techniques. Dr. Georgia Tourassi (Department of Radiology, Duke University Medical Center) provided her expertise on the medical imaging and medical decision aspects of the project. I have visited the associated University (Duke) twice for a period of 2 months total. The visit allowed for a better exposure to the medical imaging aspects of CAD systems development. The time spent at Duke University also strengthened the cooperation between the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratory at Duke University Medical Center. 3 Background 3.1 Feature-based and featureless CAD systems The study was conducted in the context of the CAD systems for breast cancer detection. Such systems utilize the information extracted from mammograms (X-ray images of breast). Two main approaches were utilized: feature-based and featureless. In the feature-based approach, a set of features is extracted from an x-ray image using an image processing algorithm. Then, the features are utilized in the process of classification of the case. Within the classifiers that can be used in this paradigm are neural networks (NN), linear discriminant analysis (LDA), Bayesian classifier, case-based classifier and others. In the featureless approach, proposed by Dr. Tourassi [17], [18], a classical information-theoretic concept of mutual information is used in order to evaluate similarity between two images. Then, case-based principles are used to classify new, incoming cases based on the similarity between the query case and cases stored in the database of the system. 4 Methods The majority of the methods and experiments are presented in the conference article which is the result of the research conducted within this project [19]. This paper is currently in press for Proceedings of SPIE Medical Imaging 2008. Therefore, only a brief summary of the methods and results are presented here. In this paper, we propose to use an ensemble technique to improve performance of previously pre- sented information-theoretic CAD system (IT-CAD). In order to do so, the database of examples is di- vided into several exclusive subsets. The division is done randomly and using k-means clustering based on 7 textural features extracted from the ROIs. Then, linear discriminant analysis (LDA) is applied to combine the outputs of classifiers built based on these subsets. It has been demonstrated in the paper that this approach provides statistically significant improvement in the performance measured using the receiver operator characteristic (ROC). In this document, two additional experiments are presented demonstrating an application of two 2 0.94 0.93 0.92 0.91 0.9 AUC 0.89 0.88 0.87 ORIGINAL PERFORMANCE 0.86 2 4 6 8 10 12 14 16 18 20 number of clusters Figure 1: Performance of the system from Experiment 1. computational intelligence techniques, artificial neural networks and fuzzy clustering, to the ensemble framework. 5 Experiments 5.1 Database For the experimental purposes mammograms extracted from Digital Database for Screening Mammog- raphy (DDSM) [20] (University of Florida) were used. The mammograms were digitized using a LU- MISYS scanner. Regions of interest (ROIs) of 512 £ 512 pixels were extracted from the mammograms. The resulting dataset consisted of 901 ROIs depicting masses and 919 ROIs depicting normal cases. The ROIs depicting normal cases were extracted from the normal mammograms in a random way. 5.2 Experiment 1: Incorporating a neural network to the ensemble system In the first experiment, feedforward neural networks (instead of LDA) were applied to combine the ensemble elements. The set of training examples was constructed using leave-one-out approach, as shown in [19]. Three hidden neurons and one output neuron were used. The activation function for all the neurons was linear. Error backpropagation with momentum was used in the training. Here, I concentrate on k-means approach to the database decomposition as it turned out to be superior over random division. To assess the performance of k-means + NN approach, 10-fold crossvalidation 3 0.94 0.93 0.92 0.91 0.9 0.89 AUC 0.88 0.87 ORIGINAL PERFORMANCE 0.86 0.85 0.84 2 4 6 8 10 12 14 16 18 20 number of clusters Figure 2: Performance of the system from Experiment 2. data handling was used to train and test the ensemble. I varied the number of elements of the ensemble between 2 and 20 with a step of 2. Figure 5.1 presents the performance of the system measured by the area under the ROC curve (AUC) for different number of elements of the ensemble. A large improvement of the performance can be clearly seen for all of the examined scenarios. 5.3 Experiment 2: Incorporating fuzzy clustering to the ensemble system In this experiment I went a step further and introduced c-means fuzzy clustering instead of previously used k-means clustering. Similarly as in Experiment 1, NN was used to combine the responses of the ensemble elements into one final decision. Ten-fold crossvalidation was used to estimate the performance of the system. The results are shown in Fig. 2. It can be seen that the performance of the c-means-based system is comparable to the performance of the system using k-means. The only two observed difference are that the system based on fuzzy c-means had noticeably lower performance than the system based on k-means when only two clusters are used but on the other hand it provided more stable performance for higher number of clusters. 6 Conclusions In this document, I presented the outcomes from the project sponsored by IEEE Computational Intel- ligence Society. The project involved cooperation between the Computational Intelligence Laboratory 4 at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center and it involved my visit at the later lab. The results of the research performed in this project were included in the conference article [19] and presented by me at the SPIE Medical Imaging 2008 conference in February 2008. Additional results were presented in this document. 7 Acknowledgments I would like to acknowledge the two professors advising me in this project who co-authored the resulting article: Dr. Jacek M. Zurada and Dr. Georgia D. Tourassi, as well as Dr. Vasilis Megaloikonomou for his help in this project. I would also like to thank the members of the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center for their support. References [1] B. van Ginneken, B. M. ter Haar Romeny, and M. A. Viergever, “Computer-aided diagnosis in chest radiography: a survey,” IEEE Transactions on Medical Imaging, vol. 20, pp. 1228–1241, 2001. [2] M. P. Sampat, M. K. Markey, and A. C. Bovik, Handbook of Image and Video Processing, 2005, ch. Computer-Aided Detection and Diagnosis in Mammography, pp. 1195–1217. [3] J. J. Perumpillichira, H. Yoshida, and D. V. Sahani, “Computer-aided detection for virtual colonoscopy.” Cancer Imaging, vol. 5, no. 1, pp. 11–16, 2005. [Online]. Available: http://dx.doi.org/10.1102/1470-7330.2005.0016 [4] J. Y. Lo, A. O. Bilska-Wolak, M. K. Markey, G. D. Tourassi, J. A. Baker, and C. E. F. Jr., Recent Advances In Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. SPIE PRESS, 2006, ch. Computer-aided diagnosis in breast imaging: where do we go after detec- tion?, pp. 871–900.
Load more

Recommended publications
  • Data Science at OLCF
    Data Science at OLCF Bronson Messer Scientific Computing Group Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Mallikarjam “Arjun” Shankar Group Leader – Advanced Data and Workflows Group Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory ORNL is managed by UT-Battelle, LLC for the US Department of Energy OLCF Data/Learning Strategy & Tactics 1. Engage with applications – Summit Early Science Applications (e.g., CANDLE) – INCITE projects (e.g., Co-evolutionary Networks: From Genome to 3D Proteome, Jacobson, et al.) – Directors Discretionary projects (e.g., Fusion RNN, MiNerva) 2. Create leadership-class analytics capabilities – Leadership analytics (e.g., Frameworks: pbdR, TensorFlow + Horovod) – Algorithms requiring scale (e.g., non-negative matrix factorization) 3. Enable infrastructure for analytics/AI and data-intensive facilities – Workflows to include data from observations for analysis within OLCF – Analytics enabling technologies (e.g., container deployments for rapidly changing DL/ML frameworks, analytics notebooks, etc.) 2 Data Science at the OLCF Applications Supported through DD/ALCC: Selected Machine Learning Projects on Titan: 2016-2017 Program PI PI Employer Project Name Allocation (Titan core-hrs) Discovering Optimal Deep Learning and Neuromorphic Network Structures using Evolutionary ALCC Robert Patton ORNL 75,000,000 Approaches on High Performance Computers ALCC Gabriel Perdue FNAL Large scale deep neural network optimization for neutrino physics 58,000,000 ALCC Gregory Laskowski GE High-Fidelity Simulations of Gas Turbine Stages for Model Development using Machine Learning 30,000,000 High-Throughput Screening and Machine Learning for Predicting Catalyst Structure and Designing ALCC Efthimions Kaxiras Harvard U. 17,500,000 Effective Catalysts ALCC Georgia Tourassi ORNL CANDLE Treatment Strategy Challenge for Deep Learning Enabled Cancer Surveillance 10,000,000 DD Abhinav Vishnu PNNL Machine Learning on Extreme Scale GPU systems 3,500,000 DD J.
    [Show full text]
  • Sophie Voisin
    Sophie Voisin Contact Geographic Information Science & Technology Office: (865) 574-8235 Information Oak Ridge National Laboratory E-mail: [email protected] Oak Ridge, TN 37831-6017 Citizenship: France US Permanent Resident Biosketch Dr. Sophie Voisin received her PhD Degree in Computer Science and Image Processing from the University of Burgundy, France, in 2008. She was a visiting scholar with the Imaging, Robotics, and Intelligent Systems Laboratory at The University of Tennessee from October 2004 to December 2008 to work on her PhD research, and subsequently was involved in program development activities. In September 2010, she joined the Oak Ridge National Laboratory (ORNL) as a Postoctoral Reseach Associate to support various efforts related to applied signal processing, 2D and 3D image understanding, and high performance computing. Firstly, she worked at the ORNL Spallation Neutron Source performing quantitative analysis of neutron image data for various industrial and academic applications related to quality control, process monitoring, and to retrieve the structure of objects. Then, she joined the ORNL Biomedical Science & Engineering Center to develop on one hand image processing algorithms for eyegaze data analysis and on the other hand text processing techniques for social media data mining to correlate individuals' health history and their geographical exposure. Noteworthily she was part of the team that received a R&D 100 award for the developement of a personalized computer aid diagnostic system relying on eyegaze analysis for decision making. Since April 2014, she has been working for the Geographic Information Science & Technology group. Her research focuses on developing multispectral image processing algorithms for CPU and GPU platforms for high performance computing of satellite imagery.
    [Show full text]
  • Dr. Georgia Tourassi Director, Health Data Sciences Institute Group
    Dr. Georgia Tourassi Director, Health Data Sciences Institute Group Leader, Biomedical Science, Engineering and Computing Group Computing, Science, and Engineering Division Oak Ridge National Laboratory Dr. Georgia Tourassi is the founding Director of the Health Data Sciences Institute and Group Leader of Biomedical Sciences, Engineering, and Computing at the Oak Ridge National Laboratory (ORNL). Concurrently, she holds appointments as an Adjunct Professor of Radiology at Duke University and the University of Tennessee Graduate School of Medicine and as a joint UT-ORNL Professor of the Bredesen Center Data Sciences Program and Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee at Knoxville. Her research interests include artificial intelligence for biomedical applications, medical imaging, biomedical informatics, clinical decision support systems, digital epidemiology, and data-driven biomedical discovery. She served on the FDA Advisory Committee and Review Panel on Computer-Aided Diagnosis Devices, and she has served as appointed Charter member and ad hoc reviewer at several NIH Study sections. Her scholarly work has led to 11 US patents and innovation disclosures and more than 250 peer-reviewed journal articles, conference proceedings articles, editorials, and book chapters. Her research has been featured in numerous high-profile publications such as the MIT Science and Technology Review, Oncology Times and the Economist. Dr. Tourassi has served as Associate Editor of the scientific journals Radiology and Neurocomputing and as a Guest Associate Editor of Medical Physics and IEEE Journal of Biomedical and Health Informatics. She is elected Fellow of the American Institute of Medical and Biological Engineering (AIMBE), the American Association of Medical Physicists (AAPM) and the International Society for Optics and Photonics (SPIE).
    [Show full text]
  • Profiling Top Most Influential Blogger Using Synonym Substitution Approach
    VASANTHAKUMAR G U et al.: PTMIBSS: PROFILING TOP MOST INFLUENTIAL BLOGGER USING SYNONYM SUBSTITUTION APPROACH PTMIBSS: PROFILING TOP MOST INFLUENTIAL BLOGGER USING SYNONYM SUBSTITUTION APPROACH Vasanthakumar G.U1, Priyanka R, Vanitha Raj K.C, Bhavani S, Asha Rani B.R, P. Deepa Shenoy and Venugopal K. R. Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, India E-mail: [email protected] Abstract fulfill their needs this way is adversely affecting the human day- Users of Online Social Network (OSN) communicate with each other, to-day lives, relationships and even families. exchange information and spread rapidly influencing others in the In recent days online social network has gained huge attention network for taking various decisions. Blog sites allow their users to from businesses point of view, where industries consider knowing create and publish thoughts on various topics of their interest in the their audience as the key to their success. Since to remain form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of competitive and noticed in the market is a need for industries and the blog documents posted by the user, they become popular. In this it has made them to engage in social networking which involves work, a novel method to profile Top Most Influential Blogger (TMIB) all age group people from various sectors. Many companies is proposed based on content analysis. Content of blog documents of provide various tools for their customers to engage and discuss bloggers under consideration in the blog network are compared and about their experience on their products, creating interactive analyzed.
    [Show full text]
  • MD Program.Fm
    Doctor of Medicine Program 24 Doctor of Medicine Program Mission Statement and the Medical Curriculum The mission of the Duke University School of Medicine is: To prepare students for excellence by first assuring the demonstration of defined core competencies. To complement the core curriculum with educational opportunities and advice regarding career planning which facilitates students to diversify their careers, from the physician-scientist to the primary care physician. To develop leaders for the twenty-first century in the research, education, and clinical practice of medicine. To develop and support educational programs and select and size a student body such that every student participates in a quality and relevant educational experi- ence. Physicians are facing profound changes in the need for understanding health, dis- ease, and the delivery of medical care changes which shape the vision of the medical school. These changes include: a broader scientific base for medical practice; a national crisis in the cost of health care; an increased number of career options for physicians yet the need for more generalists; an emphasis on career-long learning in investigative and clinical medicine; the necessity that physicians work cooperatively and effectively as leaders among other health care professionals; and the emergence of ethical issues not heretofore encountered by physicians. Medical educators must prepare physicians to respond to these changes.The most successful medical schools will position their stu- dents to take the lead addressing national health needs. Duke University School of Med- icine is prepared to meet this challenge by educating outstanding practitioners, physician scientists, and leaders. Continuing at the forefront of medical education requires more than educating Duke students in basic science, clinical research, and clinical programs for meeting the health care needs of society.
    [Show full text]
  • Use of Natural Language Processing to Extract Clinical Cancer Phenotypes From
    Author Manuscript Published OnlineFirst on August 8, 2019; DOI: 10.1158/0008-5472.CAN-19-0579 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records Authors and Affiliations: Guergana K. Savova 1,2, Ioana Danciu 3, Folami Alamudun 3, Timothy Miller 1,2, Chen Lin 1, Danielle S. Bitterman 2,4, Georgia Tourassi 3, Jeremy L. Warner 5 1. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA 2. Harvard Medical School, Boston, MA 3. Oak Ridge National Lab, Knoxville, TN 4. Dana Farber Cancer Institute, Boston, MA 5. Vanderbilt University Medical Center, Nashville, TN Running title: Natural Language Processing for Cancer Phenotypes from EMRs Corresponding author: Guergana Savova, PhD, FACMI PI Natural Language Processing Lab Boston Children's Hospital and Harvard Medical School 300 Longwood Avenue Mailstop: BCH3092 Enders 144.1 Boston, MA 02115 Tel: (617) 919-2972 Fax: (617) 730-0817 [email protected] Conflict of Interest Statement: none Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 8, 2019; DOI: 10.1158/0008-5472.CAN-19-0579 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Abstract Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for cancer patients. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment.
    [Show full text]
  • Using Case-Level Context to Classify Cancer Pathology Reports
    University of Kentucky UKnowledge Kentucky Cancer Registry Faculty Publications Cancer Registry 5-12-2020 Using Case-Level Context to Classify Cancer Pathology Reports Shang Gao Oak Ridge National Laboratory Mohammed Alawad Oak Ridge National Laboratory Noah Schaefferkoetter Oak Ridge National Laboratory Lynne Penberthy National Cancer Institute Xiao-Cheng Wu Louisiana State University See next page for additional authors Follow this and additional works at: https://uknowledge.uky.edu/kcr_facpub Part of the Computational Engineering Commons, Data Science Commons, and the Oncology Commons Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Repository Citation Gao, Shang; Alawad, Mohammed; Schaefferkoetter, Noah; Penberthy, Lynne; Wu, Xiao-Cheng; Durbin, Eric B.; Coyle, Linda; Ramanathan, Arvind; and Tourassi, Georgia, "Using Case-Level Context to Classify Cancer Pathology Reports" (2020). Kentucky Cancer Registry Faculty Publications. 1. https://uknowledge.uky.edu/kcr_facpub/1 This Article is brought to you for free and open access by the Cancer Registry at UKnowledge. It has been accepted for inclusion in Kentucky Cancer Registry Faculty Publications by an authorized administrator of UKnowledge. For more information, please contact [email protected]. Using Case-Level Context to Classify Cancer Pathology Reports Digital Object Identifier (DOI) https://doi.org/10.1371/journal.pone.0232840 Notes/Citation Information Published in PLOS ONE, v. 15, no. 5, 0232840, p. 1-21. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
    [Show full text]
  • I V Computer Aided Detection of Masses in Breast Tomosynthesis
    Computer Aided Detection of Masses in Breast Tomosynthesis Imaging Using Information Theory Principles by Swatee Singh Department of Biomedical Engineering Duke University Date:_______________________ Approved: ___________________________ Joseph Y. Lo, Ph.D., Supervisor ___________________________ Jay A. Baker, M.D. ___________________________ Kathryn R. Nightingale, Ph.D. ___________________________ Loren W. Nolte, Ph.D. ___________________________ Georgia D. Tourassi, Ph.D. Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biomedical Engineering in the Graduate School of Duke University 2008 i v ABSTRACT Computer Aided Detection of Masses in Breast Tomosynthesis Imaging Using Information Theory Principles by Swatee Singh Department of Biomedical Engineering Duke University Date:_______________________ Approved: ___________________________ Joseph Y. Lo, Ph.D., Supervisor ___________________________ Jay A. Baker, M.D. ___________________________ Kathryn R. Nightingale, Ph.D. ___________________________ Loren W. Nolte, Ph.D. ___________________________ Georgia D. Tourassi, Ph.D. An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biomedical Engineering in the Graduate School of Duke University 2008 i v Copyright by Swatee Singh 2008 Abstract Breast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer aided detection (CADe) systems can serve as a double reader to improve radiologist performance. Tomosynthesis is a limited-angle cone-beam x-ray imaging modality that is currently being investigated to overcome mammography’s limitations. CADe systems will play a crucial role to enhance workflow and performance for breast tomosynthesis. The purpose of this work was to develop unique CADe algorithms for breast tomosynthesis reconstructed volumes.
    [Show full text]
  • Envisioning Computational Innovations for Cancer Challenges
    ENVISIONING COMPUTATIONAL INNOVATIONS FOR CANCER CHALLENGES: SCOPING MEETING March 6-7, 2019 Livermore Valley Open Campus PRESENTER BIOSKETCHES Livermore, California WELCOME AND ORIENTATION Name Emily Greenspan, Ph.D. Title Program Director Affiliation National Cancer Institute Email [email protected] Dr. Greenspan works as a biomedical informatics program director in the Center for Biomedical Informatics and Information Technology (CBIIT) at NCI. Her primary role is NCI program lead for the NCI/Department of Energy collaborations, including the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium and the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program. Prior to her role at CBIIT, Emily served as a program director for the NCI's Provocative Questions Initiative in the Center for Strategic Scientific Initiatives where she also helped organize and lead several of NCI's cancer technology focused programs. Emily's grad school and postdoctoral research focused on cancer chemoprevention and arachidonic acid metabolism. 3 | P a g e WELCOME AND ORIENTATION Name Carolyn Lauzon, Ph.D. Title Program Manager Affiliation US Department of Energy Office of Science Advanced Scientific Computing Research Email [email protected] Phone (301) 903-6139 Dr. Carolyn Vea Lauzon serves in the Department of Energy, Office of Science, Office of Advanced Scientific Computing Research. Carolyn is the Facilities Program Manager for the NERSC high performance computing facility at Lawrence Berkeley National Laboratory and the DOE Office of Science lead for DOE partnerships with the National Institutes of Health and the Department of Veterans Affairs. Before joining federal service, Carolyn completed her post doctoral fellowship in Computational Medical Image Analysis at Vanderbilt University and received her PhD in Molecular Biophysics from the Johns Hopkins School of Medicine.
    [Show full text]
  • Opportunities and Obstacles for Deep Learning in Biology and Medicine
    bioRxiv preprint doi: https://doi.org/10.1101/142760; this version posted January 19, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Opportunities and obstacles for deep learning in biology and medicine A DOI-citable preprint of this manuscript is available at https://doi.org/10.1101/142760. This manuscript was automatically generated from greenelab/deep-review@a01dd71 on January 19, 2018. Authors 1,☯ 2 3 4 Travers Ching , Daniel S. Himmelstein , Brett K. Beaulieu-Jones , Alexandr A. Kalinin , 5 2 6 7 2 Brian T. Do , Gregory P. Way , Enrico Ferrero , Paul-Michael Agapow , Michael Zietz , 8,9,10 11 12 13 Michael M. Hoffman , Wei Xie , Gail L. Rosen , Benjamin J. Lengerich , Johnny 14 15 12 16 Israeli , Jack Lanchantin , Stephen Woloszynek , Anne E. Carpenter , Avanti 17 18 19,20 21 Shrikumar , Jinbo Xu , Evan M. Cofer , Christopher A. Lavender , Srinivas C. 22 17 23 24 25 Turaga , Amr M. Alexandari , Zhiyong Lu , David J. Harris , Dave DeCaprio , 15 17,26 23 27 28 Yanjun Qi , Anshul Kundaje , Yifan Peng , Laura K. Wiley , Marwin H.S. Segler , 29 30 31 32,33,† Simina M. Boca , S. Joshua Swamidass , Austin Huang , Anthony Gitter , 2,† Casey S. Greene ☯ — Author order was determined with a randomized algorithm † — To whom correspondence should be addressed: [email protected] (A.G.) and [email protected] (C.S.G.) 1. Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 2.
    [Show full text]
  • Hierarchical Neural Architectures for Classifying Cancer Pathology Reports
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2019 Hierarchical Neural Architectures for Classifying Cancer Pathology Reports Shang Gao University of Tennessee, [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Recommended Citation Gao, Shang, "Hierarchical Neural Architectures for Classifying Cancer Pathology Reports. " PhD diss., University of Tennessee, 2019. https://trace.tennessee.edu/utk_graddiss/5722 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Shang Gao entitled "Hierarchical Neural Architectures for Classifying Cancer Pathology Reports." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Computer Science. Georgia Tourassi, Major Professor We have read this dissertation and recommend its acceptance: Arvind Ramanathan, Hairong Qi, Russell Zaretzki Accepted for the Council: Dixie L. Thompson Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Hierarchical Neural Architectures for Classifying Cancer Pathology Reports A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Shang Gao December 2019 c by Shang Gao, 2019 All Rights Reserved. ii This work is dedicated to Nala, my landlord's cat, who thinks she is the queen of the universe.
    [Show full text]
  • BHI 2016 Workshop Proposal
    BHI 2016 Workshop Proposal Type: Half day Georgia Tourassi, PhD - IEEE Member Georgia Tourassi is the Director of the Health Data Sciences Institute at the Oak Ridge National Laboratory and Adjunct Professor of Radiology at Duke University Medical Center and the University of Tennessee at Knoxville. She holds a Ph.D. in Biomedical Engineering from Duke University. She is a senior member of IEEE, INNS, and SPIE, Fellow of the American Institute of Medical and Biological Engineering (AIMBE) and the American Association of Physicists in Medicine (AAPM). Bradford Hesse, PhD Bradford W. Hesse (Brad) is Chief of the Health Communication and Informatics Research Branch at the National Cancer Institute (NCI). Dr. Hesse has spent most of his career working to improve the ways in which mediated communication environments can be used to improve decision making, enhance the user experience, influence group outcomes, and to support adaptive and healthy behaviors. He has authored or co-authored over 160 publications including peer-reviewed journal articles, technical reports, books, and book chapters. His latest edited book, titled “Oncology Informatics,” is scheduled for release by Elsevier Publishing in the spring of 2016. TITLE: Web-Based Public Health Informatics Theme : Behavior and Health Informatics Keywords (Max 5): web mining, population health, outcomes research, knowledge discovery Abstract— The World Wide Web has been transforming radically the landscape of medical research and healthcare delivery. Online sources including digital social networks have facilitated a wide range of novel applications in the public health domain, including disease surveillance, early-warning, and rapid response, monitoring of population health-related interests, sentiment, and behaviors, drug performance monitoring and others.
    [Show full text]