Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

[1]  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.

[2]  R. Subramaniam,et al.  Brain PET in the Diagnosis of Alzheimer’s Disease , 2014, Clinical nuclear medicine.

[3]  O. Schillaci,et al.  Coupled Imaging with [18F]FBB and [18F]FDG in AD Subjects Show a Selective Association Between Amyloid Burden and Cortical Dysfunction in the Brain , 2018, Molecular Imaging and Biology.

[4]  Feng Lin,et al.  Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network , 2017, IEEE Transactions on Medical Imaging.

[5]  John Seibyl,et al.  Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study , 2011, The Lancet Neurology.

[6]  Jae Sung Lee,et al.  Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning , 2018, The Journal of Nuclear Medicine.

[7]  Jong Chul Ye,et al.  A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction , 2016, Medical physics.

[8]  D. Selkoe,et al.  Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid β-peptide , 2007, Nature Reviews Molecular Cell Biology.

[9]  V. Villemagne Amyloid imaging: Past, present and future perspectives , 2016, Ageing Research Reviews.

[10]  Mannudeep K. Kalra,et al.  Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) , 2017, ArXiv.

[11]  Ali Borji,et al.  Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..

[12]  Kook Cho,et al.  VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET , 2018 .

[13]  J. Seibyl,et al.  Signs and Artifacts in Amyloid PET. , 2018, Radiographics : a review publication of the Radiological Society of North America, Inc.

[14]  Hyunjin Park,et al.  Prospects of deep learning for medical imaging , 2018, Precision and Future Medicine.

[15]  Yutaro Iwamoto,et al.  Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function † , 2020, Applied Sciences.

[16]  Jerome Declerck,et al.  Quantification of 18F-florbetapir PET: comparison of two analysis methods , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[18]  A. Nordberg,et al.  Tau PET imaging in neurodegenerative tauopathies—still a challenge , 2019, Molecular Psychiatry.

[19]  A. Isaksson,et al.  Cross-validation and bootstrapping are unreliable in small sample classification , 2008, Pattern Recognit. Lett..

[20]  Cem Direkoglu,et al.  Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods , 2016 .

[21]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[22]  Alexander Hammers,et al.  Three‐dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe , 2003, Human brain mapping.

[23]  Won-Ki Jeong,et al.  Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.

[24]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[25]  Phillip M. Cheng,et al.  Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images , 2017, Journal of Digital Imaging.

[26]  Kook Cho,et al.  Classification of 18F-Florbetaben Amyloid Brain PET Image using PCA-SVM , 2019, Biomedical Science Letters.

[27]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[28]  A. Danek,et al.  Evaluation of early-phase [18F]-florbetaben PET acquisition in clinical routine cases , 2016, NeuroImage: Clinical.

[29]  D. Dowson,et al.  The Fréchet distance between multivariate normal distributions , 1982 .

[30]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[31]  R. Dobrowsky,et al.  Tau neurofibrillary pathology and microtubule stability , 2002, Journal of Molecular Neuroscience.

[32]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[33]  Eui Jin Hwang,et al.  Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs , 2019, JAMA network open.

[34]  Chris Rorden,et al.  Age-specific CT and MRI templates for spatial normalization , 2012, NeuroImage.

[35]  He Ma,et al.  An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network , 2018, IEEE Journal of Biomedical and Health Informatics.

[36]  Victor L. Villemagne,et al.  Imaginem oblivionis: the prospects of neuroimaging for early detection of Alzheimer's disease , 2005, Journal of Clinical Neuroscience.