The seven key challenges for the future of computer-aided diagnosis in medicine

[1]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[2]  Yiwen Sun,et al.  Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework , 2015, Inf. Sci..

[3]  Bram van Ginneken,et al.  Computer-aided Detection of Lung Cancer on Chest Radiographs: Effect on Observer Performance , 2012 .

[4]  L. Pachter,et al.  A New EHR Training Curriculum and Assessment for Pediatric Residents , 2017, Applied Clinical Informatics.

[5]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yong Yang,et al.  Some Results for Pythagorean Fuzzy Sets , 2015, Int. J. Intell. Syst..

[7]  R. Moynihan Preventing overdiagnosis: the myth, the music, and the medical meeting , 2015, BMJ : British Medical Journal.

[8]  Lakhmi C. Jain,et al.  Fuzzy and Neuro-Fuzzy Systems in Medicine , 2017 .

[9]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement , 2009, BMJ : British Medical Journal.

[10]  David Feeny,et al.  Training clinicians in how to use patient-reported outcome measures in routine clinical practice , 2015, Quality of Life Research.

[11]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[12]  David Dagan Feng,et al.  Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data , 2013, Journal of Digital Imaging.

[13]  Chunhua Weng,et al.  Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research , 2013, J. Am. Medical Informatics Assoc..

[14]  Xindong Peng,et al.  Pythagorean fuzzy set: state of the art and future directions , 2017, Artificial Intelligence Review.

[15]  Miki Haseyama,et al.  Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps , 2019, Comput. Biol. Medicine.

[16]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Huan Liu,et al.  Feature Selection for Classification: A Review , 2014, Data Classification: Algorithms and Applications.

[18]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Satyajit Das,et al.  Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic , 2016, Applied Intelligence.

[20]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Evangelos Triantaphyllou,et al.  The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[22]  Kyle J Myers,et al.  Evaluating imaging and computer-aided detection and diagnosis devices at the FDA. , 2012, Academic radiology.

[23]  Tolga Soyata,et al.  Utilizing Homomorphic Encryption to Implement Secure and Private Medical Cloud Computing , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[24]  John E. Kelly,et al.  Smart Machines: IBM's Watson and the Era of Cognitive Computing , 2013 .

[25]  Barbara Evans,et al.  The Challenge of Regulating Clinical Decision Support Software After 21st Century Cures , 2018, American Journal of Law & Medicine.

[26]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[27]  Ying Chen,et al.  IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. , 2016, Clinical therapeutics.

[28]  Hiroyuki Yoshida,et al.  Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use. , 2013, Medical physics.

[29]  H. Welch,et al.  Overdiagnosis in cancer. , 2010, Journal of the National Cancer Institute.

[30]  David Dagan Feng,et al.  An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification , 2017, IEEE Journal of Biomedical and Health Informatics.

[31]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[32]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, CACM.

[33]  Philip H. S. Torr,et al.  Recurrent Instance Segmentation , 2015, ECCV.

[34]  R. J. Kuo,et al.  A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis , 2018, Appl. Soft Comput..

[35]  Hoo-Chang Hoo-Chang Shin Shin,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, Ieee Transactions on Medical Imaging.

[36]  Clifford R. Jack,et al.  Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods , 2018, NeuroImage: Clinical.

[37]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[38]  A. Hardon,et al.  How to investigate the use of medicines by consumers , 2004 .

[39]  P. Embí,et al.  Toward Reuse of Clinical Data for Research and Quality Improvement: The End of the Beginning? , 2009, Annals of Internal Medicine.

[40]  E. Lander,et al.  Comprehensive assessment of cancer missense mutation clustering in protein structures , 2015, Proceedings of the National Academy of Sciences.

[41]  U. Wisløff,et al.  Cardiorespiratory Fitness, Sedentary Time, and Cardiovascular Risk Factor Clustering. , 2016, Medicine and science in sports and exercise.

[42]  Dharmendra S. Modha,et al.  Cognitive Computing , 2011, Informatik-Spektrum.

[43]  R S LEDLEY,et al.  Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. , 1959, Science.

[44]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[45]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[46]  Ronald R. Yager,et al.  Pythagorean fuzzy subsets , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[47]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[48]  D. Kopans More misinformation on breast cancer screening. , 2017, Gland surgery.

[49]  Evangelos Triantaphyllou,et al.  Prediction of Diabetes by Employing a New Data Mining Approach Which Balances Fitting and Generalization , 2008, Computer and Information Science.

[50]  Daniel Rueckert,et al.  Nonrigid Registration of Medical Images: Theory, Methods, and Applications [Applications Corner] , 2010, IEEE Signal Processing Magazine.

[51]  Xindong Peng,et al.  A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017 , 2018, Artificial Intelligence Review.

[52]  Daguang Xu,et al.  Automatic Liver Segmentation Using an Adversarial Image-to-Image Network , 2017, MICCAI.

[53]  Evangelos Triantaphyllou,et al.  Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation , 1997, Artif. Intell. Medicine.

[54]  C. Pannucci,et al.  Identifying and Avoiding Bias in Research , 2010, Plastic and reconstructive surgery.

[55]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[56]  João Manuel R S Tavares,et al.  Medical image registration: a review , 2014, Computer methods in biomechanics and biomedical engineering.

[57]  Harry Weinrauch,et al.  Computers in medicine and biology. , 1959 .

[58]  Subha Madhavan,et al.  Standard operating procedure for curation and clinical interpretation of variants in cancer , 2019, Genome Medicine.

[59]  Ioannis K. Vlachos,et al.  Intuitionistic fuzzy information - Applications to pattern recognition , 2007, Pattern Recognit. Lett..

[60]  Ricardo de Lima Thomaz,et al.  Feature extraction using convolutional neural network for classifying breast density in mammographic images , 2017, Medical Imaging.

[61]  Ching-Hsin Wang,et al.  Intuitionistic fuzzy C-regression by using least squares support vector regression , 2016, Expert Syst. Appl..

[62]  Carlos Alberto Silva,et al.  Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields , 2016, Journal of Neuroscience Methods.

[63]  Kavishwar B. Wagholikar,et al.  Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach , 2017, BMC Medical Informatics and Decision Making.

[64]  Ronald M Summers,et al.  Evaluation of computer-aided detection devices: consensus is developing. , 2012, Academic radiology.

[65]  J. Doust,et al.  Preventing overdiagnosis: how to stop harming the healthy , 2012, BMJ : British Medical Journal.

[66]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[67]  Len Lichtenfeld,et al.  Overdiagnosed: Making People Sick in Pursuit of Health , 2011 .

[68]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[69]  David K. Vawdrey,et al.  A national survey assessing the number of records allowed open in electronic health records at hospitals and ambulatory sites , 2017, J. Am. Medical Informatics Assoc..

[70]  Richard M. Karp,et al.  Reducibility Among Combinatorial Problems , 1972, 50 Years of Integer Programming.

[71]  M. Giger,et al.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. , 2008, Medical physics.

[72]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[73]  Douglas E Green,et al.  Autoflight in Ultrasonography. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[74]  James J Cimino,et al.  A Learning Health Care System Using Computer-Aided Diagnosis , 2017, Journal of medical Internet research.

[75]  Kewei Cheng,et al.  Feature Selection , 2016, ACM Comput. Surv..

[76]  Edward H. Shortliffe,et al.  A model of inexact reasoning in medicine , 1990 .

[77]  Ben J. Marafino,et al.  Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data , 2018, JAMA network open.

[78]  Hayit Greenspan,et al.  Chest pathology identification using deep feature selection with non-medical training , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[79]  Ronald R. Yager,et al.  Properties and Applications of Pythagorean Fuzzy Sets , 2016, Imprecision and Uncertainty in Information Representation and Processing.

[80]  Sue E Bowman Impact of electronic health record systems on information integrity: quality and safety implications. , 2013, Perspectives in health information management.

[81]  M. Viergever,et al.  Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. , 2016, IEEE transactions on medical imaging.

[82]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[83]  Alfredo I. Hernández,et al.  Multimodal Image Fusion for Cardiac Resynchronization Therapy Planning , 2018 .

[84]  Hon J. Yu,et al.  Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement , 2009, European Radiology.

[85]  Wei Wu,et al.  Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features , 2013, International Journal of Computer Assisted Radiology and Surgery.

[86]  C. Lehman,et al.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. , 2015, JAMA internal medicine.

[87]  Evangelos Triantaphyllou,et al.  Development and evaluation of five fuzzy multiattribute decision-making methods , 1996, Int. J. Approx. Reason..

[88]  B. van Ginneken,et al.  Computer-aided diagnosis: how to move from the laboratory to the clinic. , 2011, Radiology.

[89]  Wen-Huang Cheng,et al.  Computer-aided classification of lung nodules on computed tomography images via deep learning technique , 2015, OncoTargets and therapy.

[90]  J. Beall What I learned from predatory publishers , 2017, Biochemia Medica.

[91]  Evangelos Triantaphyllou,et al.  A systematic survey of computer-aided diagnosis in medicine: Past and present developments , 2019, Expert Syst. Appl..

[92]  Berkman Sahiner,et al.  Evaluation of computer-aided detection and diagnosis systems. , 2013, Medical physics.

[93]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[94]  Steve Halligan,et al.  CAD: how it works, how to use it, performance. , 2013, European journal of radiology.

[95]  Krassimir T. Atanassov,et al.  Intuitionistic Fuzzy Sets - Theory and Applications , 1999, Studies in Fuzziness and Soft Computing.

[96]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[97]  He Deng,et al.  Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images , 2016, Scientific Reports.

[98]  Ronald M. Summers,et al.  Pelvic artery calcification detection on CT scans using convolutional neural networks , 2017, Medical Imaging.

[99]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[100]  Ronald R. Yager,et al.  Pythagorean Membership Grades in Multicriteria Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[101]  D. Kopans The Breast Cancer Screening "Arcade" and the "Whack-A-Mole" Efforts to Reduce Access to Screening. , 2018, Seminars in ultrasound, CT, and MR.

[102]  Ali Farhadi,et al.  Unsupervised Deep Embedding for Clustering Analysis , 2015, ICML.

[103]  George Hripcsak,et al.  Next-generation phenotyping of electronic health records , 2012, J. Am. Medical Informatics Assoc..