Abstract
Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients—compared with image datasets in other computer vision fields such like faces—medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis.
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References
Chen H, Qi X, Yu L, Dou Q, Qin J, Heng P-A (2017) DCAN: Deep contour-aware networks for object instance segmentation from histology images. Med Imag Anal 36:135–146
Caravagna G, Giarratano Y, Ramazzotti D, Tomlinson I, Graham TA, Sanguinetti G, et al. (2018) Detecting repeated cancer evolution from multiregion tumor sequencing data. Nature Methods vol. 15, pp. 707–714
Du Y et al (2018) Classification of tumor epithelium and stroma by exploiting image features learned by deep convolutional neural networks, 46, 12, 1988–1999
Shin H-C et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, 35, 5, 1285–1298
Armato SG et al (2011) The lung image database consortium, (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931
Aerts H et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5 Art. no. 4006
Chollet F (2017) and Ieee, Xception: Deep Learning with Depthwise Separable Convolutions. In: 30th Ieee conference on computer vision and pattern recognition (IEEE Conference on Computer Vision and Pattern Recognition, pp 1800–1807
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21
Cheng PM, Malhi HS (2017) Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J Digit Imaging 30(2):234–243
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
Chang H, Han J, Zhong C, Snijders AM, Mao J-H, M. intelligence (2017) Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Trans Pattern Anal Mach Intell 40(5):1182–1194
Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations. Mob Netw Appl 24(1):5–17
Liu S, Liu X, Wang S, Muhammad K (2020) Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT-assisted complex environment. Neural Comput Applic
Liu S, Guo C, Al-Turjman F, Muhammad K, de Albuquerque VHC (2020) Reliability of response region: A novel mechanism in visual tracking by edge computing for IIoT environments. Mech Syst Signal Process 138:106537
Liu S, Lu MY, Li HS, Zuo YC (2019) Prediction of gene expression patterns with generalized linear regression model (in English). Front Genet 10:11 Art. no. 120
Liu S, Chen X, Li Y, Cheng XC (2019) Micro-distortion detection of lidar scanning signals based on geometric analysis (in English). Symmetry-Basel 11(12):13 Art. no. 1471
Huang C et al A dynamic priority strategy for IoV data scheduling towards key data
Chenxi H et al (2020) Sample imbalance disease classification model based on association rule feature selection
Saxe AM, McClelland JL, Ganguli S (2013) Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034
LeCun YA, Bottou L, Orr GB, Müller K-R (2012) Efficient backprop. In: Neural networks: Tricks of the trade: Springer, pp 9–48
Cruz-Roa A, Arévalo J, Judkins A, Madabhushi A, González F (2015) A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning. In: 11th International Symposium on Medical Information Processing and Analysis, vol 9681, p 968103: International Society for Optics and Photonics
Xu Y et al (2015) Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 947–951: IEEE
Ertosun MG, Rubin DL (2015) Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks. In: AMIA Annual Symposium Proceedings, vol 2015, p 1899: American Medical Informatics Association
Liu R, Hall LO, Goldgof DB, Zhou M, Gatenby RA, Ahmed KB (2016) Exploring deep features from brain tumor magnetic resonance images via transfer learning. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp 235–242: IEEE
Saha B, Gupta S, Phung D, Venkatesh S (2016) Transfer learning for rare cancer problems via discriminative sparse gaussian graphical model. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp 537–542: IEEE
Ahmed KB, Hall LO, Goldgof DB, Liu R, Gatenby RA (2017) Fine-tuning convolutional deep features for MRI based brain tumor classification. In: Medical Imaging 2017: Computer-Aided Diagnosis, vol 10134, p 101342E: International Society for Optics and Photonics
Lao J et al (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme, 7, 1, 10353
Chato L, Latifi S (2017) Machine learning and deep learning techniques to predict overall survival of brain tumor patients using MRI images. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), pp 9–14: IEEE
Shen L, Anderson T (2017) Multimodal brain MRI tumor segmentation via convolutional neural networks, ed
Ghafoorian M et al (2017) Transfer learning for domain adaptation in mri: Application in brain lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 516–524
Li H, Parikh NA, He L (2018) A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci 12
Puranik M, Shah H, Shah K, Bagul S (2018) Intelligent Alzheimer’s Detector using Deep Learning and IEEE (Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems). IEEE, New York, pp 318–323
Rachmadi MF, Valdés-Hernández MdC, Komura T (2018) Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI. In: International Workshop on PRedictive Intelligence In MEdicine, Springer, pp 85–93
Wong KCL, Syeda-Mahmood T, Moradi M (2018) Building medical image classifiers with very limited data using segmentation networks (in English). Med Image Anal 49:105–116
Yang Y et al (2018) Glioma grading on conventional MR images: a deep learning study with transfer learning. Front Neurosci 12
Cheng B, Liu M, Zhang D, Shen D (2019) Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease. Brain Imaging Behav 13(1):138–153
Lu S, Lu Z, Zhang Y-D (2019) Pathological brain detection based on AlexNet and transfer learning. J Comput Sci 30:41–47
Talo M, Baloglu UB, Yıldırım Ö, Rajendra Acharya U (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 54:176–188
Dar SUH, Özbey M, Çatlı AB, Çukur T (2020) A transfer-learning approach for accelerated MRI using deep neural networks. Magn Reson Med 84:663–685
Saba T, Sameh Mohamed A, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221–230
Li JP, Qiu S, Shen YY, Liu CL, He HG (2020) Multisource transfer learning for cross-subject EEG emotion recognition. IEEE Trans Cybern 50(7):3281–3293
Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS (2020) Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 122 Art. no. 103804
De Cooman T, Varon C, Van de Vel A, Ceulemans B, Lagae L, Van Huffel S (2017) Semi-supervised one-class transfer learning for heart rate based epileptic seizure detection, in 2017 Computing in Cardiology (CinC), pp. 1–4: IEEE
Margeta J, Criminisi A, Cabrera Lozoya R, Lee DC, Ayache N (2017) Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput Methods Biomech Biomed Eng: Imaging Vis 5(5):339–349
Al Rahhal MM, Bazi Y, Al Zuair M, Othman E, BenJdira BJJOM (2018) Convolutional neural networks for electrocardiogram classification. B. Eng 38(6):1014–1025
Giffard-Roisin S et al (2018) Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy, vol. 66, no. 2, pp. 343–353
Murugesan B et al (2018) Ecgnet: Deep network for arrhythmia classification, In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6: IEEE
Salem M, Taheri S, Yuan JS (2018) ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features, In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4: IEEE
Alquran H, Alqudah A, Abu-Qasmieh I, Al-Badarneh A, Almashaqbeh SJNNW (2019) ECG classification using higher order spectral estimation and deep learning techniques, vol. 29, no. 4, pp. 207–219
Byeon Y-H, Pan S-B, Kwak K-CJS (2019) Intelligent deep models based on scalograms of electrocardiogram signals for biometrics, vol. 19, no. 4, p. 935
Tadesse GA et al (2019) Cardiovascular disease diagnosis using cross-domain transfer learning, In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4262–4265: IEEE
Diker A, Cömert Z, Avcı E, Toğaçar M, Ergen B (2019) A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification,” In 2019 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1–6: IEEE
Cao X-C, Yao B, Chen B-QJIA (2019) Atrial fibrillation detection using an improved multi-Scale decomposition enhanced residual convolutional neural network, vol. 7, pp. 89152–89161
Jiang F et al (2019) A Transfer Learning Approach to Detect Paroxysmal Atrial Fibrillation Automatically Based on Ballistocardiogram Signal, vol. 9, no. 9, pp. 1943–1949
Van Steenkiste G, van Loon G, Crevecoeur G. JSR (2020) Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture, vol. 10, no. 1, pp. 1–12
Mazo C, Bernal J, Trujillo M, Alegre E (2018) Transfer learning for classification of cardiovascular tissues in histological images. Comput Methods Prog Biomed 165:69–76
Dietlmeier J, McGuinness K, Rugonyi S, Wilson T, Nuttall A, O’Connor NEJPRL (2019) Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data, vol. 128, pp. 521–528
Miyagawa M, Costa MGF, Gutierrez MA, Costa JPGF, Filho CFFJIAC (2019) Detecting Vascular Bifurcation in IVOCT Images Using Convolutional Neural Networks With Transfer Learning, vol. 7, pp. 66167–66175
Blanquer I, Brasileiro F, Brito A, Calatrava A, Carvalho A, Fetzer C, Figueiredo F, Guimarães RP, Marinho L, Meira W Jr, Silva A, Alberich-Bayarri Á, Camacho-Ramos E, Jimenez-Pastor A, Ribeiro ALL, Nascimento BR, Silva F (Sep 2020) Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure. Future Generation Comput Syst Int J Esci 110:119–134
Vu CC, Siddiqui ZA, Zamdborg L, Thompson AB, Quinn TJ, Castillo E, Guerrero TM (2020) Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning. J Appl Clin Med Phys 21(6):108–113
Huynh BQ, Li H, Giger MLJJOMI (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks, vol. 3, no. 3, p. 034501
Kandaswamy C, Silva LM, Alexandre LA, Santos JMJJOBS (2016) High-content analysis of breast cancer using single-cell deep transfer learning, vol. 21, no. 3, pp. 252–259
Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha KJMP (2016) Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography, vol. 43, no. 12, pp. 6654–6666
Dhungel N, Carneiro G, Bradley APJMIA (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention, vol. 37, pp. 114–128
Kooi T, van Ginneken B, Karssemeijer N, den Heeten AJMP (2017) Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network, vol. 44, no. 3, pp. 1017–1027
Samala RK et al (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms, vol. 62, no. 23, p. 8894
Yap MH et al (2017) Automated breast ultrasound lesions detection using convolutional neural networks, vol. 22, no. 4, pp. 1218–1226
Chougrad H, Zouaki H, Alheyane OJCM (2018) Deep convolutional neural networks for breast cancer screening. P. I. Biomedicine 157:19–30
Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu SJMP (2018) A deep learning method for classifying mammographic breast density categories, vol. 45, no. 1, pp. 314–321
Samala RK, Chan H-P, Hadjiiski L, Helvie MA, Richter CD, Cha KHJITOMI (2018) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets, vol. 38, no. 3, pp. 686–696
Samala RK et al (2018) Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis, vol. 63, no. 9, p. 095005
Zhang J, Chen B, Zhou M, Lan H, Gao FJIA (2018) Photoacoustic image classification and segmentation of breast cancer: A feasibility study, vol. 7, pp. 5457–5466
Byra M et al (2019) Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, vol. 46, no. 2, pp. 746–755
Khan S, Islam N, Jan Z, Din I. U, Rodrigues JJCJPRL (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning, vol. 125, pp. 1–6
Mendel K, Li H, Sheth D, Giger MJAR (2019) Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography, vol. 26, no. 6, pp. 735–743
Xie J, Liu R, Luttrell IV J, Zhang CJFIG (2019) Deep learning based analysis of histopathological images of breast cancer, vol. 10, p. 80
Yu S, Liu L, Wang Z, Dai G, Xie YJSCTS (2019) Transferring deep neural networks for the differentiation of mammographic breast lesions, vol. 62, no. 3, pp. 441–447
Zhu Z et al (2019) Deep learning for identifying radiogenomic associations in breast cancer, vol. 109, pp. 85–90
Zhu Z et al (2019) Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ, vol. 115, p. 103498
Chaves E, Goncalves CB, Albertini MK, Lee S, Jeon G, Fernandes HC (Jun 2020) Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images. Appl Opt 59(17):E23–E28
Chen P, Chen Y, Deng Y, Wang Y, He P, Lv X, Yu J (Aug 2020) A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images. Int J Comput Assist Radiol Surg 15(8):1407–1415
Chougrad H, Zouaki H, Alheyane O (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180
Hu QY, Whitney HM, Giger ML (2020) A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep 10(1)
Sawada Y, Kozuka K (2015) Transfer learning method using multi-prediction deep boltzmann machines for a small scale dataset, In 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 110–113: IEEE
Shouno H, Suzuki S, Kido S (2015) A transfer learning method with deep convolutional neural network for diffuse lung disease classification, In International Conference on Neural Information Processing, pp. 199–207: Springer
Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou SJIJOB (2016) H Informatics, Multisource transfer learning with convolutional neural networks for lung pattern analysis, vol. 21, no. 1, pp. 76–84
Paul R et al (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma, vol. 2, no. 4, p. 388
Seelan LJ, Suresh LP, Veni SK (2016) Automatic extraction of Lung lesion by using optimized toboggan based approach with feature normalization and transfer learning methods, In 2016 International Conference on Emerging Technological Trends (ICETT), pp. 1–10: IEEE
Shen W et al (2016) Learning from experts: Developing transferable deep features for patient-level lung cancer prediction, In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 124–131: Springer
Nibali A, He Z, Wollersheim DJIJOCAR (2017) Pulmonary nodule classification with deep residual networks. Surgery 12(10):1799–1808
Hussein S, Cao K, Song Q, Bagci U (2017) Risk stratification of lung nodules using 3D CNN-based multi-task learning, In International conference on information processing in medical imaging, pp. 249–260: Springer
Shan H, Wang G, Kalra MK, de Souza R, Zhang J (2017) Enhancing transferability of features from pretrained deep neural networks for lung nodule classification, In Proceedings of the 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Wang C, Elazab A, Wu J, Hu QJCMI (2017) Lung nodule classification using deep feature fusion in chest radiography. Graphics 57:10–18
da Nóbrega RVM, Peixoto SA, da Silva SPP, Rebouças Filho PP (2018) Lung nodule classification via deep transfer learning in CT lung images, In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pp. 244–249: IEEE
Hosny A et al (2018) Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study, vol. 15, no. 11, p. e1002711
Dey R, Lu Z, Hong Y (2018) Diagnostic classification of lung nodules using 3D neural networks, In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 774–778: IEEE
Fang T (2018) A novel computer-aided lung cancer detection method based on transfer learning from GoogLeNet and median intensity projections, In 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET), pp. 286–290: IEEE
Nishio M et al (2018) Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning, vol. 13, no. 7
Hussein S, Kandel P, Bolan CW, Wallace MB, Bagci UJITOMI (2019) Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches, vol. 38, no. 8, pp. 1777–1787
Lakshmi D, Thanaraj KP, Arunmozhi MJIJOIS (2019) Technology Convolutional neural network in the detection of lung carcinoma using transfer learning approach
Li Y, Zhang L, Chen H, Yang NJIA (2019) Lung nodule detection with deep learning in 3D thoracic MR images, vol. 7, pp. 37822–37832
Shi Z et al (2019) A deep CNN based transfer learning method for false positive reduction, vol. 78, no. 1, pp. 1017–1033
Zhang S et al (2019) Computer-aided diagnosis (CAD) of pulmonary nodule of thoracic CT image using transfer learning, vol. 32, no. 6, pp. 995–1007
Huang X, Lei Q, Xie T, Zhang Y, Hu Z, Zhou QJAPA (2020) Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images
Wankhade NV, Patey MA (2013) Transfer learning approach for learning of unstructured data from structured data in medical domain, In 2013 2nd International Conference on Information Management in the Knowledge Economy, pp. 86–91: IEEE
Marsh JN et al (2018) Deep learning global glomerulosclerosis in transplant kidney frozen sections, vol. 37, no. 12, pp. 2718–2728
Zheng Q, Tastan G, Fan Y (2018) Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data, In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1487–1490: IEEE
Zheng Q, Furth SL, Tasian GE, Fan YJJOPU (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features, vol. 15, no. 1, pp. 75. e1–75. e7
Efremova DB, Konovalov DA, Siriapisith T, Kusakunniran W, Haddawy PJAPA (2019) Automatic segmentation of kidney and liver tumors in CT images
Hao P-Y et al (2019) Texture branch network for chronic kidney disease screening based on ultrasound images, pp. 1–10
Kannan S et al (2019) Segmentation of glomeruli within trichrome images using deep learning, vol. 4, no. 7, pp. 955–962
Kuo C-C et al (2019) Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning, vol. 2, no. 1, pp. 1–9
Wu Z et al (2019) PASnet: A Joint Convolutional Neural Network for Noninvasive Renal Ultrasound Pathology Assessment, In 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology (ICBCB), pp. 96–99: IEEE
Yin S et al (2018) Subsequent boundary distance regression and pixelwise classification networks for automatic kidney segmentation in ultrasound images
Yin S et al (2020) Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks, vol. 60, p. 101602
Ayyar M, Mathur P, Shah RR, Sharma SG (2018) Harnessing ai for kidney glomeruli classification, In 2018 IEEE International Symposium on Multimedia (ISM), pp. 17–20: IEEE
Mathur P, Ayyar M, Shah RR, Sharma S (2019) Exploring Classification of Histological Disease Biomarkers from Renal Biopsy Images, In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 81–90: IEEE
Huang C, Lu Y, Lan Y, Chen S, Guo S, Zhang G (2020) Automatic segmentation of bioabsorbable vascular stents in intravascular optical coherence images using weakly supervised attention network, Futur Gener Comput Syst, 2020/07/27/
Huang C et al (2020) A Deep Segmentation Network of Multi-scale Feature Fusion based on Attention Mechanism for IVOCT Lumen Contour, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. PP, pp. 1–1, 02/14
Huang C et al (2019) A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm. Front Neurosci 13:03/20
Huang C et al (2020) A New Transfer Function for Volume Visualization of Aortic Stent and Its Application to Virtual Endoscopy. J ACM Trans Multimedia Comput Commun Appl 16(2s %):Article 65
Huang C et al (2019) Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography. Complexity 2019:1–10, 12/23
Huang C et al (2018) A New Framework for the Integrative Analytics of Intravascular Ultrasound and Optical Coherence Tomography Images, IEEE Access, vol. PP, pp. 1–1, 05/22
da Nóbrega RVM, Reboucas PP, Rodrigues MB, da Silva SPP, Dourado C, de Albuquerque VHC (2020) Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Applic 32(15):11065–11082
Lin F et al (2020) A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma. Eur J Radiol 129 Art. no. 109079
Acknowledgements
This study is partially supported by Royal Society International Exchanges Cost Share Award, UK (RP202G0230); Medical Research Council Confidence in Concept Award, UK (MC_PC_17171); Hope Foundation for Cancer Research, UK (RM60G0680); Fundamental Research Funds for the Central Universities (CDLS-2020-03); Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education.
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Wang, J., Zhu, H., Wang, SH. et al. A Review of Deep Learning on Medical Image Analysis. Mobile Netw Appl 26, 351–380 (2021). https://doi.org/10.1007/s11036-020-01672-7
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DOI: https://doi.org/10.1007/s11036-020-01672-7