1. 贵州省智能医学影像分析与精准诊断重点实验室,贵州 贵阳 550025
2. 贵州大学计算机科学与技术学院,贵州 贵阳 550025
[ "王丽会(1982- ),女,博士,贵州大学计算机科学与技术学院、贵州省智能医学影像分析与精准诊断重点实验室副教授,主要研究方向为医学成像、机器学习与深度学习、医学图像处理、计算机视觉" ]
[ "秦永彬(1980- ),男,博士,贵州大学计算机科学与技术学院、贵州省智能医学影像分析与精准诊断重点实验室教授,主要研究方向为大数据治理与应用、文本计算与认知智能" ]
网络首发:2020-11,
纸质出版:2020-11-15
移动端阅览
王丽会, 秦永彬. 深度学习在医学影像中的研究进展及发展趋势[J]. 大数据, 2020,6(6):2020056-1.
Lihui WANG, Yongbin QIN. State of the art and future perspectives of the applications of deep learning in the medical image analysis[J]. Big Data Research, 2020, 6(6): 2020056-1.
王丽会, 秦永彬. 深度学习在医学影像中的研究进展及发展趋势[J]. 大数据, 2020,6(6):2020056-1. DOI: 10.11959/j.issn.2096-0271.2020056.
Lihui WANG, Yongbin QIN. State of the art and future perspectives of the applications of deep learning in the medical image analysis[J]. Big Data Research, 2020, 6(6): 2020056-1. DOI: 10.11959/j.issn.2096-0271.2020056.
医学影像是临床诊断的重要辅助工具,医学影像数据占临床数据的90%,因此,充分挖掘医学影像信息将对临床智能诊断、智能决策以及预后起到重要的作用。随着深度学习的出现,利用深度神经网络分析医学影像已成为目前研究的主流。根据医学影像分析的流程,从医学影像数据的产生、医学影像的预处理,到医学影像的分类预测,充分阐述了深度学习在每一环节的应用研究现状,并根据其面临的问题,对未来的发展趋势进行了展望。
Medical imaging is an important auxiliary tool for clinical diagnosis.Medical images occupy almost 90% of clinical data.Therefore
mining medical image information will be beneficial for intelligent diagnosis
decision-making and prognosis prediction.With the emergence of deep learning
using deep neural networks to analyze medical images has become a hot research topic.Following the process of medical image analysis
from the image acquisition
the image pre-processing to the classification and prediction
the state-of-the-art applications of deep learning in each step of medical image analysis was elaborated
and according to the existed issues and the challenges
the future perspectives were finally discussed.
GONDARA L , . Medical image denoising using convolutional denoising autoencoders [C ] // 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).[S.l.:s.n] . 2016 : 241 - 246 .
CHEN H , ZHANG Y , KALRA M K , et al . Low-dose CT with a residual encoderdecoder convolutional neural network [J ] . IEEE Transactions on Medical Imaging , 2017 , 36 ( 12 ): 2524 - 2535 .
KANG E , MIN J H , YE J C . A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction [J ] . Medical Physics , 2017 , 44 ( 10 ): 360 - 375 .
YANG Q S , YAN P K , ZHANG Y B , et al . Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss [J ] . IEEE Transactions on Medical Imaging , 2018 , 37 ( 6 ): 1348 - 1357 .
YOU C Y , YANG Q S , SHAN H M , et al . Structurally-sensitive multi-scale deep neural network for low-dose CT denoising [J ] . IEEE Access , 2018 , 6 : 41839 - 41855 .
MA Y J , WEI B , FENG P , et al . Low-dose CT image denoising using a generative adversarial network with a hybrid loss function for noise learning [J ] . IEEE Access , 2020 , 8 : 67519 - 67529 .
YIN X R , ZHAO Q L , LIU J , et al . Domain progressive 3D residual convolution network to improve low-dose CT imaging [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 12 ): 2903 - 2913 .
WU D F , GONG K , KIM K , et al . Consensus neural network for medical imaging denoising with only noisy training samples [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . ] , 2019 : 741 - 749 .
GEORGESCU M I , IONESCU R T , VERGA N . Convolutional neural networks with intermediate loss for 3D super-resolution of CT and MRI scans [J ] . IEEE Access , 2020 , 8 : 49112 - 49124 .
OKTAY O , BAI W J , LEE M , et al . Multiinput cardiac image super-resolution using convolutional neural networks [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2016 : 246 - 254 .
PHAM C H,DÍEZ C T , MEUNIER H , et al . Multiscale brain MRI super-resolution using deep 3D convolutional networks [J ] . Computerized Medical Imaging and Graphics , 2019 :77.
MCDONAGH S , HOU B , ALANSARYET A , et al . Context-sensitive super-resolution for fast fetal magnetic resonance imaging [C ] // Molecular Imaging,Reconstruction and Analysis of Moving Body Organs,and Stroke Imaging and Treatment . Cham:Springer , 2017 : 116 - 126 .
ZHENG Y , ZHEN B , CHEN A , et al . A hybrid convolutional neural network for super-resolution reconstruction of MR images [J ] . Medical Physics , 2020 , 47 ( 7 ): 3013 - 3022 .
ZHAO X L , ZHANG Y L , ZHANG T , et al . Channel splitting network for single MR image super-resolution [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 11 ): 5649 - 5662 .
TANNO R , WORRALL D E , GHOSH A , et al . Bayesian image quality transfer with CNNs:exploring uncertainty in dMRI super-resolution [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2017 : 611 - 619 .
PENG C , LIN W A , LIAO H F , et al . SAINT:spatially aware interpolation network for medical slice synthesis [C ] // The IEEE/CVF Conference on Computer Vision and Pattern Recognition.[S.l.:s.n] . 2020 : 7750 - 7759 .
SHI J , LIU Q P , WANG C F , et al . Superresolution reconstruction of MR image with a novel residual learning network algorithm [J ] . Physics in Medicine &Biology , 2018 , 63 ( 8 ):85011.
LYU Q , SHAN H M , STEBER C , et al . Multi-contrast super-resolution MRI through a progressive network [J ] . IEEE Transactions on Medical Imaging , 2020 , 39 ( 9 ): 2738 - 2749 .
YANG Y , SUN J , LI H B , et al . ADMMCSNet:a deep learning approach for image compressive sensing [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 42 ( 3 ): 521 - 538 .
ADLER J,ÖKTEM O . Learned primaldual reconstruction [J ] . IEEE Transactions on Medical Imaging , 2018 , 37 ( 6 ): 1322 - 1332 .
CHENG J , WANG H F , YING L , et al . Model learning:primal dual networks for fast MR imaging [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . 2019 : 21 - 29 .
ZHANG H M , DONG B , LIU B D . JSRNet:a deep network for joint spatial-radon domain CT reconstruction from incomplete data [C ] // IEEE International Conference on Acoustics,Speech and Signal Processing.[S.l.:s.n] . 2019 : 3657 - 3661 .
LEE D , YOO J , YE J C . Deep residual learning for compressed sensing MRI [C ] // 2017 IEEE 14th International Symposium on Biomedical Imaging . Piscataway:IEEE Press , 2017 : 15 - 18 .
LEE D , YOO J , TAK S , et al . Deep residual learning for accelerated MRI using magnitude and phase networks [J ] . IEEE Transactions on Biomedical Engineering , 2018 , 65 ( 9 ): 1985 - 1995 .
SCHLEMPER J , CABALLERO J , HAJNAL J V , et al . A deep cascade of convolutional neural networks for dynamic MR image reconstruction [J ] . IEEE Transactions on Medical Imaging , 2017 , 37 ( 2 ): 491 - 503 .
HAN Y , YOO J , KIM H H , et al . Deep learning with domain adaptation for accelerated projection‐reconstruction MR [J ] . Magnetic Resonance in Medicine , 2018 , 80 ( 3 ): 1189 - 1205 .
EO T , JUN Y , KIM T , et al . KIKI‐Net:cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images [J ] . Magnetic Resonance in Medicine , 2018 , 80 ( 5 ): 2188 - 2201 .
BAO L J , YE F Z , CAI C B , et al . Undersampled MR image reconstruction using an enhanced recursive residual network [J ] . Journal of Magnetic Resonance , 2019 , 305 : 232 - 246 .
DAI Y X , ZHUANG P X . Compressed sensing MRI via a multi-scale dilated residual convolution network [J ] . Journal of Magnetic Resonance Imaging , 2019 , 63 : 93 - 104 .
YANG G , YU S M , DONG H , et al . DAGAN:deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction [J ] . IEEE Transactions on Medical Imaging , 2017 , 37 ( 6 ): 1310 - 1321 .
QUAN T M , NGUYEN-DUC T , JEONG W K . Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss [J ] . IEEE Transactions on Medical Imaging , 2018 , 37 ( 6 ): 1488 - 1497 .
MARDANI M , GONG E H , CHENG J Y , et al . Deep generative adversarial neural networks for compressive sensing MRI [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 1 ): 167 - 179 .
KITCHEN A , SEAH J . Deep generative adversarial neural networks for realistic prostate lesion MRI synthesis [J ] . arXiv preprint,2017,arXiv:1708.00129 ,
SCHLEGL T , SEEBÖCK P , WALDSTEIN S M , et al . Unsupervised anomaly detection with generative adversarial networks to guide marker discovery [C ] // International Conference on Information Processing in Medical Imaging . Cham:Springer , 2017 : 146 - 157 .
CHUQUICUSMA M J M , HUSSEIN S , BURT J , et al . How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis [C ] // 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).[S.l.:s.n] . 2018 : 240 - 244 .
FRID-ADAR M , DIAMANT I , KLANG E , et al . GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification [J ] . Neurocomputing , 2018 , 321 : 321 - 331 .
BERMUDEZ C , PLASSARD A J , DAVIS L T , et al . Learning implicit brain MRI manifolds with deep learning [C ] // SPIE Medical Imaging Conference.[S.l.:s.n] . 2018 .
BAUR C , ALBARQOUNI S , NAVAB N . MelanoGANs:high resolution skin lesion synthesis with GANs [J ] . arXiv preprint,2018,arXiv:1804.04338 ,
KORKINOF D , RIJKEN T,O’NEILL M , et al . High-resolution mammogram synthesis using progressive generative adversarial networks [J ] . arXiv preprint,2018,arXiv:1807.03401 ,
KANG E , CHANG W , YOO J , et al . Deep convolutional framelet denosing for lowdose CT via wavelet residual network [J ] . IEEE Transactions on Medical Imaging , 2018 , 37 ( 6 ): 1358 - 1369 .
WOLTERINK J M , LEINER T , VIERGEVER M A , et al . Generative adversarial networks for noise reduction in low-dose CT [J ] . IEEE Transactions on Medical Imaging , 2017 , 36 ( 12 ): 2536 - 2545 .
BAHRAMI K , SHI F , ZONG X P , et al . Reconstruction of 7T-like images from 3T MRI [J ] . IEEE Transactions on Medical Imaging , 2016 , 35 ( 9 ): 2085 - 2097 .
BAHRAMI K , REKIK I , SHI F , et al . Joint reconstruction and segmentation of 7T-like MR images from 3T MRI based on cascaded convolutional neural networks [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . 2017 : 764 - 772 .
NIE D , CAO X H , GAO Y Z , et al . Estimating CT image from MRI data using 3D fully convolutional networks [C ] // Deep Learning and Data Labeling for Medical Applications . Cham:Springer , 2016 : 170 - 178 .
NIE D , TRULLO R , PETITJEAN C , et al . Medical image synthesis with context-aware generative adversarial networks [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . 2017 : 417 - 425 .
ISOLA P , ZHU J Y , ZHOU T , et al . Image-to-image translation with conditional adversarial networks [C ] // The IEEE Conference on Computer Vision and Pattern Recognition.[S.l.:s.n] . 2017 : 1125 - 1134 .
ZHU J Y , PARK T , ISOLA P , et al . Unpaired image-to-image translation using cycle-consistent adversarial networks [C ] // The IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 2223 - 2232 .
MASPERO M , SAVENIJE M , DINKLA A M , et al . Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy [J ] . Physics in Medicine & Biology , 2018 , 63 ( 18 ):185001
CHOI H , LEE D S . Generation of structural MR images from amyloid PET:application to MR-less quantification [J ] . Journal of Nuclear Medicine , 2018 , 59 ( 7 ): 1111 - 1117 .
WOLTERINK J M , DINKLA A M , SAVENIJE M H F , et al . Deep MR to CT synthesis using unpaired data [C ] // International Workshop on Simulation and Synthesis in Medical Imaging . Cham:Springer , 2017 : 14 - 23 .
CHARTSIAS A , JOYCE T , DHARMAKUMAR R , et al . Adversarial image synthesis for unpaired multimodal cardiac data [C ] // International Workshop on Simulation and Synthesis in Medical Imaging.[S.l.:s.n] . 2017 : 3 - 13 .
ZHENG J M , CAO J W , WANG Z X , et al . Semi-automatic synthetic computed tomography generation for abdomens using transfer learning and semisupervised classification [J ] . Journal of Medical Imaging and Health Informatics , 2019 , 9 ( 9 ): 1878 - 1886 .
JIN C B , JUNG W , JOO S , et al . Deep CT to MR synthesis using paired and unpaired data [J ] . Sensors , 2019 , 19 ( 10 ):2361.
HIASA Y , OTAKE Y , TAKAO M , et al . Cross-modality image synthesis from unpaired data using CycleGAN [C ] // International Workshop on Simulation and Synthesis in Medical Imaging.[S.l.:s.n] . 2018 : 31 - 41 .
CHENG X , ZHANG L , ZHENG Y F . Deep similarity learning for multimodal medical images [J ] . Computer Methods in Biomechanics and Biomedical Engineering:Imaging & Visualization , 2018 , 6 ( 3 ): 248 - 252 .
SEDGHI A , LUO J , MEHRTASH A , et al . Semi-supervised deep metrics for image registration [J ] . arXiv preprint,2018,arXiv:1804.01565. ,
SIMONOVSKY M , GUTIÉRREZBECKER B , MATEUS D , et al . A deep metric for multimodal registration [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . 2016 : 10 - 18 .
MIAO S , WANG Z J , ZHENG Y F , et al . Real-time 2D/3D registration viaCNN regression [C ] // 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) . Piscataway:IEEE Press , 2016 : 1430 - 1434 .
MIAO S , WANG Z J , LIAO R . A CNN regression approach for real-time 2D/3D registration [J ] . IEEE Transactions on Medical Imaging , 2016 , 35 ( 5 ): 1352 - 1363 .
SALEHI S S M , KHAN S , ERDOGMUS D , et al . Real-time deep pose estimation with geodesic loss for image-totemplate rigid registration [J ] . IEEE Transactions on Medical Imaging , 2018 , 38 ( 2 ): 470 - 481 .
ZHENG J N , MIAO S , WANG Z J , et al . Pairwise domain adaptation module for CNN-based 2-D/3-D registration [J ] . Journal of Medical Imaging , 2018 , 5 ( 2 ):21204.
YANG X , KWITT R , STYNER M , et al . Quicksilver:fast predictive image registration–a deep learning approach [J ] . Neuroimage , 2017 , 158 : 378 - 396 .
HU Y P , GIBSON E , GHAVAMI N , et al . Label-driven weakly-supervised learning for multimodal deformable image registration [C ] // 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).[S.l.:s.n] . 2018 : 1070 - 1074 .
HERING A , KUCKERTZ S , HELDMANN S , et al . Enhancing label-driven deep deformable image registration with local distance metrics for state-ofthe-art cardiac motion tracking [M ] // Bildverarbeitung für die Medizin 2019 . Wiesbaden:Springer Vieweg , 2019 : 309 - 314 .
CAO X H , YANG J H , WANG L , et al . Deep learning based inter-modality image registration supervised by intramodality similarity [C ] // International Workshop on Machine Learning in Medical Imaging . Cham:Springer , 2018 : 55 - 63 .
FAN J F , CAO X H , YAP P T , et al . BIRNet:brain image registration using dual-supervised fully convolutional networks [J ] . Medical Image Analysis , 2019 , 54 : 193 - 206 .
JADERBERG M , SIMONYAN K , ZISSERMAN A . Spatial transformer networks [C ] // The 28th International Conference on Neural Information Processing Systems . New York:ACM Press , 2015 : 2017 - 2025 .
YOO I , HILDEBRAND D G C , TOBIN W F , et al . ssEMnet:serial-section electron microscopy image registration using a spatial transformer network with learned features [C ] // Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Cham:Springer , 2017 : 249 - 257 .
BALAKRISHNAN G , ZHAO A , SABUNCU M R , et al . VoxelMorph:a learning framework for deformable medical image registration [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 8 ): 1788 - 1800 .
ZHAO A , BALAKRISHNAN G , DURAND F , et al . Data augmentation using learned transformations for one-shot medical image segmentation [C ] // The IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 8543 - 8553 .
KUANG D , SCHMAH T . FAIM–a ConvNet method for unsupervised 3D medical image registration [M ] // Machine Learning in Medical Imaging.[S.l.:s.n] . 2019 : 646 - 654 .
ZHANG J . Inverse-consistent deep networks for unsupervised deformable image registration [J ] . arXiv preprint,2018,arXiv1809.03443 ,
TANG K , LI Z , TIAN L L , et al . ADMIR–affine and deformable medical image registration for drug-addicted brain images [J ] . IEEE Access , 2020 , 8 : 70960 - 70968 .
YAN P K , XU S , RASTINEHAD A R , et al . Adversarial image registration with application for MR and TRUS image fusion [C ] // International Workshop on Machine Learning in Medical Imaging . Cham:Springer , 2018 : 197 - 204 .
MAHAPATRA D , ANTONY B , SEDAI S , et al . Deformable medical image registration using generative adversarial networks [C ] // 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . Piscataway:IEEE Press , 2018 : 1449 - 1453 .
TANNER C , OZDEMIR F , PROFANTER R , et al . Generative adversarial networks for MR-CT deformable image registration [J ] . arXiv preprint,2018,arXiv:1807.07349 ,
MOESKOPS P , WOLTERINK J M , VAN DER VELDEN B H M , et al . Deep learning for multi-task medical image segmentation in multiple modalities [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2016 : 478 - 486 .
LI X M , DOU Q , CHEN H , et al . 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images [J ] . Medical Image Analysis , 2018 , 45 : 41 - 54 .
BI L , KIM J , KUMAR A , et al . Stacked fully convolutional networks with multichannel learning:application to medical image segmentation [J ] . The Visual Computer:International Journal of Computer Graphics , 2017 , 33 ( 6-8 ): 1061 - 1071 .
ZHOU X Y , SHEN M , RIGA C , et al . Focal FCN:towards small object segmentation with limited training data [J ] . arXiv preprint,2017,arXiv:1711.01506 ,
ZENG G D , ZHENG G Y . Multistream 3D FCN with multi-scale deep supervision for multi-modality isointense infant brain MR image segmentation [C ] // 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . Piscataway:IEEE Press , 2018 : 136 - 140 .
POUDEL R P K , LAMATA P , MONTANA G . Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation [C ] // Reconstruction,Segmentation,and Analysis of Medical Images . Cham:Springer , 2016 : 83 - 94 .
ZHOU Z W , SIDDIQUEE M M R , TAJBAKHSH N , et al . Unet++:a nested U-Net architecture for medical image segmentation [C ] // Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Cham:Springer , 2018 : 3 - 11 .
MILLETARI F , NAVAB N , AHMADI S A . V-Net:fully convolutional neural networks for volumetric medical image segmentation [C ] // 2016 4th International Conference on 3D Vision(3DV) . Piscataway:IEEE Press , 2016 : 565 - 571 .
ALOM M Z , HASAN M , YAKOPCIC C , et al . Recurrent residual convolutional neural network based on U-Net (R2UNet) for medical image segmentation [J ] . arXiv preprint,2018,arXiv:1802.06955 ,
XIE Y P , ZHANG Z Z , SAPKOTA M , et al . Spatial clockwork recurrent neural network for muscle perimysium segmentation [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention . Cham:Springer , 2016 : 185 - 193 .
AZAD R , ASADI-AGHBOLAGHI M , FATHY M , et al . Bi-directional ConvLSTM U-net with densley connected convolutions [C ] // 2019 IEEE/CVF International Conference on Computer Vision Workshop . Piscataway:IEEE Press , 2019 : 406 - 415 .
CHEN J X , YANG L , ZHANG Y Z , et al . Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation [C ] // Advances in Neural Information Processing Systems.[S.l.:s.n] . 2016 : 3036 - 3044 .
XUE Y , XU T , ZHANG H , et al . SegAN:adversarial network with multi-scale L 1 loss for medical image segmentation [J ] . Neuroinformatics , 2018 , 16 ( 3-4 ): 383 - 392 .
SINGH V K , RASHWAN H A , ROMANI S , et al . Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network [J ] . Expert Systems with Applications , 2020 , 139 :112855.
ZHANG C Y , SONG Y , LIU S D , et al . MSGAN:GAN-based semantic segmentation of multiple sclerosis lesions in brain magnetic resonance imaging [C ] // 2018 Digital Image Computing:Techniques and Applications (DICTA) . Piscataway:IEEE Press , 2018 : 1 - 8 .
TAGHANAKI S A , ZHENG Y F , ZHOU S K , et al . Combo loss:handling input and output imbalance in multi-organ segmentation [J ] . Computerized Medical Imaging and Graphics , 2019 , 75 : 24 - 33 .
韩冬 , 李其花 , 蔡巍 , 等 . 人工智能在医学影像中的研究与应用 [J ] . 大数据 , 2019 , 5 ( 1 ): 39 - 67 .
HAN D , LI Q H , CAI W , et al . Research and application of artificial intelligence in medical imaging [J ] . Big Data Research , 2019 , 5 ( 1 ): 39 - 67 .
SHANTHI T , SABEENIAN R S . Modified AlexNet architecture for classification of diabetic retinopathy images [J ] . Computers& Electrical Engineering , 2019 , 76 : 56 - 64 .
LI X C , SHEN L L , XIE X P , et al . Multiresolution convolutional networks for chest X-ray radiograph based lung nodule detection [J ] . Artificial Intelligence in Medicine , 2020 , 103 :101744.
MAHBOD A , SCHAEFER G , ELLINGER I , et al . Fusing fine-tuned deep features for skin lesion classification [J ] . Computerized Medical Imaging and Graphics , 2019 , 71 : 19 - 29 .
CHRISTODOULIDIS S , ANTHIMOPOULOS M , EBNER L , et al . Multisource transfer learning with convolutional neural networks for lung pattern analysis [J ] . IEEE Journal of Biomedical and Health Informatics , 2016 , 21 ( 1 ): 76 - 84 .
HARSONO I W , LIAWATIMENA S , CENGGORO T W . Lung nodule detection and classification from thorax CT-scan using RetinaNet with transfer learning [J ] . Journal of King Saud UniversityComputer and Information Sciences,2020:Accepted ,
ALKHALEEFAH M , MA S C , CHANG Y L , et al . Double-shot transfer learning for breast cancer classification from X-ray images [J ] . Applied Sciences , 2020 , 10 ( 11 ):3999.
ABBAS A , ABDELSAMEA M M , GABER M M . Detrac:transfer learning of class decomposed medical images in convolutional neural networks [J ] . IEEE Access , 2020 , 8 : 74901 - 74913 .
OKSUZ I , RUIJSINK B , PUYOLANTÓN E , et al . Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning [J ] . Medical Image Analysis , 2019 , 55 : 136 - 147 .
GUAN Q J , HUANG Y P , ZHONG Z , et al . Diagnose like a radiologist:attention guided convolutional neural network for thorax disease classification [J ] . arXiv preprint,2018,arXiv:1801.09927. .
GONZÁLEZ-DÍAZ I . Dermaknet:incorporating the knowledge of dermatologists to convolutional neural networks for skin lesion diagnosis [J ] . IEEE Journal of Biomedical and Health Informatics , 2018 , 23 ( 2 ): 547 - 559 .
LI L , XU M , WANG X F , et al . Attention based glaucoma detection:a largescale database and CNN model [C ] // The IEEE Conference on Computer Vision and Pattern Recognition.[S.l.:s.n] . 2019 : 10571 - 10580 .
FANG L Y , WANG C , LI S T , et al . Attention to lesion:Lesion-aware convolutional neural network for retinal optical coherence tomography image classification [J ] . IEEE Transactions on Medical Imaging , 2019 , 38 ( 8 ): 1959 - 1970 .
MITSUHARA M , FUKUI H , SAKASHITA Y , et al . Embedding human knowledge in deep neural network via attention map [J ] . arXiv preprint,2019,arXiv:1905.03540 .
MAJTNER T , YILDIRIM-YAYILGAN S , HARDEBERG J Y . Combining deep learning and hand-crafted features for skin lesion classification [C ] // 2016 6th International Conference on Image Processing Theory,Tools and Applications . Piscataway:IEEE Press , 2016 : 1 - 6 .
CHAI Y D , LIU H Y , XU J . Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models [J ] . Knowledge-Based Systems , 2018 , 161 : 147 - 156 .
XIE Y T , XIA Y , ZHANG J P , et al . Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT [J ] . IEEE Transactions on Medical Imaging , 2018 , 38 ( 4 ): 991 - 1004 .
YAN K , WANG X S , LU L , et al . DeepLesion:automated mining of large-scale lesion annotations and universal lesion detection with deep learning [J ] . Journal of Medical Imaging , 2018 , 5 ( 3 ):36501.
XUE Z , JAEGER S , ANTANI S , et al . Localizing tuberculosis in chest radiographs with deep learning [C ] // Medical Imaging 2018:Imaging Informatics for Healthcare,Research,and Applications.[S.l.:s.n] . 2018 .
DING J , LI A X , HU Z Q , et al . Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks [C ] // International Conference on Medical Image Computing and ComputerAssisted Intervention . Cham:Springer , 2017 : 559 - 567 .
LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 42 ( 2 ): 318 - 327 .
LOTTER W , DIAB A R , HASLAM B , et al . Robust breast cancer detection in mammography and digital breast tomosynthesis using annotationefficient deep learning approach [J ] . arXiv preprint,2019,arXiv:1912.11027 ,
MERCAN C , BALKENHOL M , VAN DER LAAK J , et al . From point annotations to epithelial cell detection in breast cancer histopathology using RetinaNet [C ] // International Conference on Medical Imaging with Deep Learning-Extended Abstract Track.[S.l.:s.n] . 2019 .
ZLOCHA M , DOU Q , GLOCKER B . Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels [C ] // International Conference on Medical Image Computing and Computer-Assisted Intervention.[S.l.:s.n] . 2019 : 402 - 410 .
SANTERAMO R , WITHEY S , MONTANA G . Longitudinal detection of radiological abnormalities with timemodulated LSTM [C ] // Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Cham:Springer , 2018 : 326 - 333 .
SU Y T , LU Y , CHEN M , et al . Spatiotemporal joint mitosis detection using CNN-LSTM network in time-lapse phase contrast microscopy images [J ] . IEEE Access , 2017 , 5 : 18033 - 18041 .
GAO R Q , HUO Y K , BAO S X , et al . Distanced LSTM:time-distanced gates in long short-term memory models for lung cancer detection [C ] // International Workshop on Machine Learning in Medical Imaging.[S.l.:s.n] . 2019 : 310 - 318 .
SHIN H C , ROTH H R , GAO M C , et al . Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning [J ] . IEEE Transactions on Medical Imaging , 2016 , 35 ( 5 ): 1285 - 1298 .
ZHANG R K , ZHENG Y L , MAK T W C , et al . Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain [J ] . IEEE Journal of Biomedical and Health Informatics , 2016 , 21 ( 1 ): 41 - 47 .
JESSON A , GUIZARD N , GHALEHJEGH S H , et al . CASED:curriculum adaptive sampling for extreme data imbalance [C ] // International Conference on Medical Image Computing and ComputerAssisted Intervention . Cham:Springer , 2017 : 639 - 646 .
LIU J Y , CAO L L , AKIN O , et al . 3DFPNHS 2:3D feature pyramid network based high sensitivity and specificity pulmonary nodule detection [C ] // Medical Image Computing and Computer Assisted Intervention-MICCAI 2019 . Cham:Springer , 2019 : 513 - 521 .
0
浏览量
2049
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621