Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities
暂无分享,去创建一个
[1] Yu Qiao,et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.
[2] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[3] Mark Sanderson,et al. How do health care professionals select medical images they need? , 2012, Aslib Proc..
[4] Rich Caruana,et al. Multitask Learning , 1997, Machine Learning.
[5] D. Jaffray,et al. Principles of Magnetic Resonance Imaging , 2012, Radiation research.
[6] Nour Eldeen M. Khalifa,et al. Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset , 2019, Int. J. Reason. based Intell. Syst..
[7] Pedro Antonio Gutiérrez,et al. Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images , 2016, IEEE Transactions on Medical Imaging.
[8] Muhammad N. Marsono,et al. 2-D DWT System Architecture for Image Compression , 2015, J. Signal Process. Syst..
[9] Pamela C. Cosman,et al. Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.
[10] N. Altorki,et al. Current state of imaging for lung cancer staging. , 2004, Thoracic surgery clinics.
[11] Walid Al-Dhabyani,et al. Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images , 2019, International Journal of Advanced Computer Science and Applications.
[12] E. Hall,et al. Radiobiology for the radiologist , 1973 .
[13] Michele Larobina,et al. Medical Image File Formats , 2014, Journal of Digital Imaging.
[14] Frank Y. Shih,et al. Robust watermarking and compression for medical images based on genetic algorithms , 2005, Inf. Sci..
[15] Steven C. H. Hoi,et al. Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Richard L Ehman,et al. Blueprint for imaging in biomedical research. , 2007, Radiology.
[17] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[18] Navid Farahani,et al. whole slide imaging in pathology: advantages, limitations, and emerging perspectives , 2015 .
[19] Alfredo De Santis,et al. Cloud-based adaptive compression and secure management services for 3D healthcare data , 2015, Future Gener. Comput. Syst..
[20] Qi Xia,et al. Searchable Public-Key Encryption with Data Sharing in Dynamic Groups for Mobile Cloud Storage , 2015, J. Univers. Comput. Sci..
[21] Sebastian Thrun,et al. Is Learning The n-th Thing Any Easier Than Learning The First? , 1995, NIPS.
[22] A. Tirado-Ramos,et al. Model Formulation: Information Object Definition-based Unified Modeling Language Representation of DICOM Structured Reporting: A Case Study of Transcoding DICOM to XML , 2002, J. Am. Medical Informatics Assoc..
[23] F. Terrier,et al. Modern approach of diagnosis and management of acute flank pain: review of all imaging modalities. , 2002, European urology.
[24] Bruno Carpentieri,et al. A Secure Low Complexity Approach for Compression and Transmission of 3-D Medical Images , 2013, 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications.
[25] Ukrit M.Ferni,et al. A Survey on Lossless Compression for Medical Images , 2011 .
[26] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] R A Robb,et al. Analyze: a comprehensive, operator-interactive software package for multidimensional medical image display and analysis. , 1989, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[28] Yaozong Gao,et al. Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy , 2014, IEEE Transactions on Medical Imaging.
[29] Xin Qi,et al. Exploring automatic prostate histopathology image gleason grading via local structure modeling , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[30] J-C Lapayre,et al. Neurology diagnostics security and terminal adaptation for PocketNeuro project. , 2008, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.
[31] Heung-Il Suk,et al. Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.
[32] Hamid R. Tizhoosh,et al. Autoencoding the retrieval relevance of medical images , 2015, 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA).
[33] Li Wang,et al. Massive parallel implementation of JPEG2000 decoding algorithm with multi-GPUs , 2014, Sensing Technologies + Applications.
[34] D. Vanel,et al. Role of imaging to choose treatment , 2005, Cancer imaging : the official publication of the International Cancer Imaging Society.
[35] David Blinder,et al. JPEG 2000-based compression of fringe patterns for digital holographic microscopy , 2014 .
[36] D K Das,et al. MRF‐ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images , 2017, Journal of microscopy.
[37] T. Lawson,et al. Radiology of the Pancreas , 1982, Springer New York.
[38] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[39] J. Anthony Seibert,et al. Modalities and Data Acquisition , 2009 .
[40] Junichi Hara. An implementation of JPEG 2000 interactive image communication system , 2005, 2005 IEEE International Symposium on Circuits and Systems.
[41] Steven C. Horii,et al. Review: Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging , 1997, J. Am. Medical Informatics Assoc..
[42] Aleksey Boyko,et al. Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.
[43] Armando J. Pinho,et al. Progressive Lossy-to-Lossless Compression of DNA Microarray Images , 2016, IEEE Signal Processing Letters.
[44] Gorka Bastarrika,et al. Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest. , 2007, Chest.
[45] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[46] E. Halpern,et al. Targeted biopsy of the prostate: the impact of color Doppler imaging and elastography on prostate cancer detection and Gleason score. , 2007, Urology.
[47] Chein-I Chang,et al. An Automatic Computer-Aided Detection System for Meniscal Tears on Magnetic Resonance Images , 2009, IEEE Transactions on Medical Imaging.
[48] D. Brenner,et al. Computed tomography--an increasing source of radiation exposure. , 2007, The New England journal of medicine.
[49] Mona Kathryn Garvin,et al. Multimodal segmentation of optic disc and cup from stereo fundus and SD-OCT images , 2013, Medical Imaging.
[50] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[51] Barton F. Branstetter. Practical Imaging Informatics: Foundations and Applications for PACS Professionals , 2009 .
[52] Anil Vasdev Parwani,et al. Digital imaging in pathology. , 2012, Clinics in laboratory medicine.
[53] B. Erickson,et al. Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[54] Eugene Tseytlin,et al. Digital Pathology Consultations—a New Era in Digital Imaging, Challenges and Practical Applications , 2013, Journal of Digital Imaging.
[55] Med Salim Bouhlel,et al. Using ROI with ISOM compression to medical image , 2016, Int. J. Comput. Vis. Robotics.
[56] Daisuke Komura,et al. Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.
[57] Michael W. Marcellin,et al. Compression Based on a Joint Task-Specific Information Metric , 2015, 2015 Data Compression Conference.
[58] Nour Eldeen M. Khalifa,et al. Deep Iris: Deep Learning for Gender Classification Through Iris Patterns , 2019, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.
[59] Peter Schelkens,et al. Wavelet based volumetric medical image compression , 2015, Signal Process. Image Commun..
[60] Michael W. Marcellin,et al. The Current Role of Image Compression Standards in Medical Imaging , 2017, Inf..
[61] Bipin C. Desai,et al. Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion , 2008, Comput. Medical Imaging Graph..
[62] S. Bhavani,et al. Comparison of fractal coding methods for medical image compression , 2013, IET Image Process..
[63] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[64] Walid Al-Dhabyani,et al. Dataset of breast ultrasound images , 2019, Data in brief.
[65] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[66] Noriyuki Tomiyama,et al. Effect of Matrix Size on the Image Quality of Ultra-high-resolution CT of the Lung: Comparison of 512 × 512, 1024 × 1024, and 2048 × 2048. , 2018, Academic radiology.
[67] Roman Starosolski,et al. New simple and efficient color space transformations for lossless image compression , 2014, J. Vis. Commun. Image Represent..
[68] B. Hillman. Introduction to the special issue on medical imaging in oncology. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[69] Guang-Zhong Yang,et al. Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.
[70] Yi Li,et al. Cancer Metastasis Detection With Neural Conditional Random Field , 2018, ArXiv.
[71] Antonio Plaza,et al. Graphics processing unit implementation of JPEG2000 for hyperspectral image compression , 2012 .
[72] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[73] Konstantinos Kamnitsas,et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks , 2016, IPMI.
[74] Dayong Wang,et al. Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.
[75] A. Suruliandi,et al. Empirical evaluation of EZW and other encoding techniques in the wavelet-based image compression domain , 2015, Int. J. Wavelets Multiresolution Inf. Process..
[76] Matthew T. Freedman,et al. Artificial convolution neural network techniques and applications for lung nodule detection , 1995, IEEE Trans. Medical Imaging.
[77] Angel Cruz-Roa,et al. High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection , 2018, PloS one.
[78] Arun Krishnan,et al. Robust Learning-Based Parsing and Annotation of Medical Radiographs , 2011, IEEE Transactions on Medical Imaging.
[79] Peter Hufnagl,et al. Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology) , 2008, Diagnostic pathology.
[80] C. Gatsonis,et al. Cancer yield of mammography, MR, and US in high-risk women: prospective multi-institution breast cancer screening study. , 2007, Radiology.
[81] L. Fass. Imaging and cancer: A review , 2008, Molecular oncology.
[82] David Dagan Feng,et al. Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data , 2013, Journal of Digital Imaging.
[83] J. Mifflin. Visual archives in perspective: enlarging on historical medical photographs. , 2007, The American archivist.
[84] Noel C. F. Codella,et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[85] Syed Muhammad Anwar,et al. Medical Image Analysis using Convolutional Neural Networks: A Review , 2017, Journal of Medical Systems.
[86] I. Maglogiannis,et al. Region of Interest Coding Techniques for Medical Image Compression , 2007, IEEE Engineering in Medicine and Biology Magazine.