Accelerating Super-Resolution and Visual Task Analysis in Medical Images
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Sameer Antani | Ghada Zamzmi | Sivaramakrishnan Rajaraman | Ghada Zamzmi | S. Rajaraman | Sameer Kiran Antani
[1] Andre Mouton,et al. On the relevance of denoising and artefact reduction in 3D segmentation and classification within complex computed tomography imagery. , 2019, Journal of X-ray science and technology.
[2] Farida Cheriet,et al. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images , 2016, Comput. Medical Imaging Graph..
[3] Chang-Woo Ryu,et al. Comparison of Low- and Standard-Dose CT for the Diagnosis of Acute Appendicitis: A Meta-Analysis. , 2017, AJR. American journal of roentgenology.
[4] Radu Tudor Ionescu,et al. Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans , 2020, IEEE Access.
[5] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[6] Vasudevan Lakshminarayanan,et al. Application of an enhanced deep super-resolution network in retinal image analysis , 2020, BiOS.
[7] S. Tashiro,et al. Estimation of the effects of medical diagnostic radiation exposure based on DNA damage , 2018, Journal of radiation research.
[8] Andrey S. Krylov,et al. Image Interpolation by Super-Resolution , 2006 .
[9] William T. Freeman,et al. Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.
[10] Neeraj Sharma,et al. Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network , 2008, Journal of medical physics.
[11] Clement J. McDonald,et al. Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration , 2014, IEEE Transactions on Medical Imaging.
[12] Carol C Wu,et al. Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia. , 2019, Radiology. Artificial intelligence.
[13] Lin Zheng,et al. Fast low-dose Computed Tomography image Super-Resolution Reconstruction via Sparse coding and Random Forests , 2019, 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).
[14] Changhee Han,et al. Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection , 2019, 2019 International Conference on 3D Vision (3DV).
[15] Paramartha Dutta,et al. Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey , 2020, Knowl. Based Syst..
[16] Xiaoou Tang,et al. Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.
[17] Wei Wang,et al. Deep Learning for Single Image Super-Resolution: A Brief Review , 2018, IEEE Transactions on Multimedia.
[18] Samet Aymaz,et al. Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule , 2020, Multimedia Tools and Applications.
[19] Dinggang Shen,et al. Machine Learning in Medical Imaging , 2012, Lecture Notes in Computer Science.
[20] Russell C. Hardie,et al. A Computationally Efficient U-Net Architecture for Lung Segmentation in Chest Radiographs , 2019, 2019 IEEE National Aerospace and Electronics Conference (NAECON).
[21] Kyoung Mu Lee,et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Juan Yu,et al. Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection , 2019, Artif. Intell. Medicine.
[23] Weisheng Li,et al. Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization , 2020, Biomed. Signal Process. Control..
[24] Giancarlo Mauri,et al. A novel framework for MR image segmentation and quantification by using MedGA , 2019, Comput. Methods Programs Biomed..
[25] Horst Bischof,et al. Fast and accurate image upscaling with super-resolution forests , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Zhou Wang,et al. On the Mathematical Properties of the Structural Similarity Index , 2012, IEEE Transactions on Image Processing.
[27] Kin-Man Lam,et al. Image Super-resolution via Feature-augmented Random Forest , 2017, Signal Process. Image Commun..
[28] Steven C. H. Hoi,et al. Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Dwarikanath Mahapatra,et al. Progressive Generative Adversarial Networks for Medical Image Super resolution , 2019, ArXiv.
[30] Guang Li,et al. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) , 2018, IEEE Transactions on Medical Imaging.
[31] R. Keys. Cubic convolution interpolation for digital image processing , 1981 .
[32] Xingying Li,et al. Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion , 2019, EURASIP J. Image Video Process..
[33] Giancarlo Mauri,et al. MedGA: A novel evolutionary method for image enhancement in medical imaging systems , 2019, Expert Syst. Appl..
[34] Hui Fan,et al. Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN , 2019, Comput. Intell. Neurosci..
[35] Zhou Wang,et al. Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[36] Kohei Arai. Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019, Volume 1 , 2020, CVC.
[37] P. Salminen,et al. The Accuracy of Low-dose Computed Tomography Protocol in Patients With Suspected Acute Appendicitis: The OPTICAP Study. , 2020, Annals of surgery.
[38] Stefan Jaeger,et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. , 2014, Quantitative imaging in medicine and surgery.
[39] Nicolas Papadakis,et al. GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision , 2019, 1907.10085.
[40] K. Doi,et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. , 2000, AJR. American journal of roentgenology.