Deep multi-scale resemblance network for the sub-class differentiation of adrenal masses on computed tomography images
Tài liệu tham khảo
Young, 2007, The incidentally discovered adrenal mass, N Engl J Med, 356, 601, 10.1056/NEJMcp065470
Halefoglu, 2010, Differentiation of adrenal adenomas from nonadenomas using CT histogram analysis method: a prospective study, Eur J Radiol, 73, 643, 10.1016/j.ejrad.2008.12.010
Mayo-Smith, 2001, State-of-the-art adrenal imaging, Radiographics, 21, 995, 10.1148/radiographics.21.4.g01jl21995
Grumbach, 2003, Management of the clinically inapparent adrenal mass (incidentaloma), Ann Intern Med, 138, 424, 10.7326/0003-4819-138-5-200303040-00013
Doi, 2007, Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Comput Med Imaging Graph, 31, 198, 10.1016/j.compmedimag.2007.02.002
Song, 2013, Feature-based image patch approximation for lung tissue classification, IEEE Trans Med Imaging, 32, 797, 10.1109/TMI.2013.2241448
Sorensen, 2010, Quantitative analysis of pulmonary emphysema using local binary patterns, IEEE Trans Med Imaging, 29, 559, 10.1109/TMI.2009.2038575
Bi, 2014, Multi-stage thresholded region classification for whole-body PET-CT lymphoma studies, 569
Chen, 2017, Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images, IEEE Trans Med Imaging, 36, 802, 10.1109/TMI.2016.2629462
Han, 2015, Texture feature analysis for computer-aided diagnosis on pulmonary nodules, J Digit Imaging, 28, 99, 10.1007/s10278-014-9718-8
Dhara, 2016, A combination of shape and texture features for classification of pulmonary nodules in lung CT images, J Digit Imaging, 29, 466, 10.1007/s10278-015-9857-6
Xu, 2006, MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies, IEEE Trans Med Imaging, 25, 464, 10.1109/TMI.2006.870889
Yao, 2011, Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification, Acad Radiol, 18, 306, 10.1016/j.acra.2010.11.013
Ko, 2011, X-ray image classification using random forests with local wavelet-based CS-local binary patterns, J Digit Imaging, 24, 1141, 10.1007/s10278-011-9380-3
Depeursinge, 2012, Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames, IEEE Trans Inf Technol Biomed, 16, 665, 10.1109/TITB.2012.2198829
Bagci, 2012, Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans, IEEE Trans Biomed Eng, 59, 1620, 10.1109/TBME.2012.2190984
Song, 2012, A multistage discriminative model for tumor and lymph node detection in thoracic images, IEEE Trans Med Imaging, 31, 1061, 10.1109/TMI.2012.2185057
Sorensen, 2012, Texture-based analysis of COPD: a data-driven approach, IEEE Trans Med Imaging, 31, 70, 10.1109/TMI.2011.2164931
Korfiatis, 2010, Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT, IEEE Trans Inf Technol Biomed, 14, 675, 10.1109/TITB.2009.2036166
Li, 2018, Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting, IEEE J Biomed Health Inform, 22, 215, 10.1109/JBHI.2017.2703890
Lee, 2010, Random forest based lung nodule classification aided by clustering, Comput Med Imaging Graph, 34, 535, 10.1016/j.compmedimag.2010.03.006
Gray, 2013, Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, NeuroImage, 65, 167, 10.1016/j.neuroimage.2012.09.065
Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans Pattern Anal Mach Intell, 35, 1798, 10.1109/TPAMI.2013.50
Anthimopoulos, 2016, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Trans Med Imaging, 35, 1207, 10.1109/TMI.2016.2535865
Dou, 2016, Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks, IEEE Trans Med Imaging, 35, 1182, 10.1109/TMI.2016.2528129
Wang, 2018, Multiscale rotation-invariant convolutional neural networks for lung texture classification, IEEE J Biomed Health Inform, 22, 184, 10.1109/JBHI.2017.2685586
Zhang, 2019, Medical image classification using synergic deep learning, Med Image Anal, 54, 10, 10.1016/j.media.2019.02.010
Yu, 2018, Melanoma recognition in dermoscopy images via aggregated deep convolutional features, IEEE Trans Biomed Eng, 66, 1006, 10.1109/TBME.2018.2866166
Ahn, 2016, X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid, 855
Kumar, 2017, An ensemble of fine-tuned convolutional neural networks for medical image classification, IEEE J Biomed Health Inform, 21, 31, 10.1109/JBHI.2016.2635663
Frid-Adar, 2018, GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321, 10.1016/j.neucom.2018.09.013
Ahn, 2019, Convolutional sparse kernel network for unsupervised medical image analysis, Med Image Anal, 56, 140, 10.1016/j.media.2019.06.005
Zhang, 2019, Attention residual learning for skin lesion classification, IEEE Trans Med Imaging, 38, 2092, 10.1109/TMI.2019.2893944
He, 2016, Deep residual learning for image recognition, 770
Wu, 2019, Wider or deeper: revisiting the resnet model for visual recognition, Pattern Recogn, 90, 119, 10.1016/j.patcog.2019.01.006
Hadsell, 2006, Dimensionality reduction by learning an invariant mapping, 1735
Dean, 2012, Large scale distributed deep networks, Adv Neural Inf Proces Syst, 1223
Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans Intell Syst Technol, 2, 27, 10.1145/1961189.1961199
Hoo-Chang, 2016, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans Med Imaging, 35, 1285, 10.1109/TMI.2016.2528162
Simonyan, 2014, Very deep convolutional networks for large-scale image recognition
Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579
De Herrera, 2016
Müller, 2012, Overview of the ImageCLEF 2012 medical image retrieval and classiFIcation tasks, 1
Yu, 2017, Deep transfer learning for modality classification of medical images, Information, 8, 91, 10.3390/info8030091
Zhang, 2017, Classification of medical images in the biomedical literature by jointly using deep and handcrafted visual features, IEEE J Biomed Health Inform, 22, 1521, 10.1109/JBHI.2017.2775662
Koitka, 2016, Traditional feature engineering and deep learning approaches at medical classification task of ImageCLEF 2016, 304