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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning.
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2016
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S. Miao, Z. J. Wang, and R. Liao, “A cnn regression approach for real-time 2d/3d registration,” IEEE transactions on medical imaging , vol. 35, no. 5, pp. 1352–1363, 2016
2016
Cited alongside, same era.
G. Wu, M. Kim, Q. Wang, B. C. Munsell, and D. Shen, “Scalable high-performance image registration framework by unsupervised deep feature representations learning,” IEEE Transactions on Biomedical Engineering , vol. 63, no. 7, pp. 1505–1516, 2016
2016
Cited alongside, same era.
Q. Meng, C. Baumgartner, M. Sinclair, J. Housden, M. Rajchl, A. Gomez, B. Hou, N. Toussaint, V. Zimmer, J. Tan et al. , “Automatic shadow detection in 2d ultrasound images,” in Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis . Springer, 2018, pp. 66–75
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
P. Bándi, O. Geessink, Q. Manson, M. van Dijk, M. Balkenhol, M. Hermsen, B. E. Bejnordi, B. Lee, K. Paeng, A. Zhong et al. , “From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge,” IEEE Transactions on Medical Imaging , 2018
2018
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N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler et al. , “Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic),” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 168–172
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, “A deep learning model integrating fcnns and crfs for brain tumor segmentation,” Medical image analysis , vol. 43, pp. 98–111, 2018
2018
Later among the works it cites.
T. Nair, D. Precup, D. L. Arnold, and T. Arbel, “Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 655–663
2018
Later among the works it cites.
2018
Later among the works it cites.
R. Robinson, O. Oktay, W. Bai, V. V. Valindria, M. M. Sanghvi, N. Aung, J. M. Paiva, F. Zemrak, K. Fung, E. Lukaschuk et al. , “Subject-level prediction of segmentation failure using real-time convolutional neural nets,” 2018
2018
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2018
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2018
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K. Men, T. Zhang, X. Chen, B. Chen, Y. Tang, S. Wang, Y. Li, and J. Dai, “Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning,” Physica Medica , vol. 50, pp. 13–19, 2018
2018
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J. Lee and R. M. Nishikawa, “Automated mammographic breast density estimation using a fully convolutional network,” Medical physics , vol. 45, no. 3, pp. 1178–1190, 2018
2018
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2018
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J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin et al. , “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature medicine , vol. 24, no. 9, p. 1342, 2018
2018
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J. Duan, J. Schlemper, W. Bai, T. J. Dawes, G. Bello, G. Doumou, A. De Marvao, D. P. O’Regan, and D. Rueckert, “Deep nested level sets: Fully automated segmentation of cardiac mr images in patients with pulmonary hypertension,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 595–603
2018
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W. Bai, H. Suzuki, C. Qin, G. Tarroni, O. Oktay, P. M. Matthews, and D. Rueckert, “Recurrent neural networks for aortic image sequence segmentation with sparse annotations,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 586–594
2018
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2018
Later among the works it cites.
T. Joyce, A. Chartsias, and S. A. Tsaftaris, “Deep multi-class segmentation without ground-truth labels,” 2018
2018
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R. LaLonde and U. Bagci, “Capsules for object segmentation,” arXiv preprint arXiv:1804.04241 , 2018
2018
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C.-M. Nam, J. Kim, and K. J. Lee, “Lung nodule segmentation with convolutional neural network trained by simple diameter information,” 2018
2018
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2018
Later among the works it cites.
H. R. Roth, C. Shen, H. Oda, M. Oda, Y. Hayashi, K. Misawa, and K. Mori, “Deep learning and its application to medical image segmentation,” Medical Imaging Technology , vol. 36, no. 2, pp. 63–71, 2018
2018
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2018
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2018
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M. P. Heinrich, O. Oktay, and N. Bouteldja, “Obelisk-one kernel to solve nearly everything: Unified 3d binary convolutions for image analysis,” 2018
2018
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M. P. Heinrich, M. Blendowski, and O. Oktay, “Ternarynet: faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions,” International journal of computer assisted radiology and surgery , pp. 1–10, 2018
2018
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2018
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N. Tong, S. Gou, S. Yang, D. Ruan, and K. Sheng, “Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks,” Medical physics , vol. 45, no. 10, pp. 4558–4567, 2018
2018
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2018
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2018
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2018
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S. M. R. Al Arif, K. Knapp, and G. Slabaugh, “Spnet: Shape prediction using a fully convolutional neural network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 430–439
2018
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2018
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Z. Mirikharaji and G. Hamarneh, “Star shape prior in fully convolutional networks for skin lesion segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 737–745
2018
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F. Ambellan, A. Tack, M. Ehlke, and S. Zachow, “Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the osteoarthritis initiative,” Medical Image Analysis , 2018
2018
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S. C. van de Leemput, M. Prokop, B. van Ginneken, and R. Manniesing, “Stacked bidirectional convolutional lstms for 3d non-contrast ct reconstruction from spatiotemporal 4d ct,” 2018
2018
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K. Lønning, P. Putzky, M. W. Caan, and M. Welling, “Recurrent inference machines for accelerated mri reconstruction,” 2018
2018
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A. Sheikhjafari, M. Noga, K. Punithakumar, and N. Ray, “Unsupervised deformable image registration with fully connected generative neural network,” 2018
2018
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B. Hou, N. Miolane, B. Khanal, M. Lee, A. Alansary, S. McDonagh, J. Hajnal, D. Rueckert, B. Glocker, and B. Kainz, “Deep pose estimation for image-based registration,” 2018
2018
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B. Hou, B. Khanal, A. Alansary, S. McDonagh, A. Davidson, M. Rutherford, J. V. Hajnal, D. Rueckert, B. Glocker, and B. Kainz, “Image-based registration in canonical atlas space,” 2018
2018
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2018
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G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca, “An unsupervised learning model for deformable medical image registration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9252–9260
2018
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P. Costa, A. Galdran, M. I. Meyer, M. Niemeijer, M. Abràmoff, A. M. Mendonça, and A. Campilho, “End-to-end adversarial retinal image synthesis,” IEEE transactions on medical imaging , vol. 37, no. 3, pp. 781–791, 2018
2018
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D. Mahapatra, B. Antony, S. Sedai, and R. Garnavi, “Deformable medical image registration using generative adversarial networks,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 1449–1453
2018
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H. Tang, A. Pan, Y. Yang, K. Yang, Y. Luo, S. Zhang, and S. H. Ong, “Retinal image registration based on robust non-rigid point matching method,” Journal of Medical Imaging and Health Informatics , vol. 8, no. 2, pp. 240–249, 2018
2018
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L. Pan, F. Shi, W. Zhu, B. Nie, L. Guan, and X. Chen, “Detection and registration of vessels for longitudinal 3d retinal oct images using surf,” in Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging , vol. 10578. International Society for Optics and Photonics, 2018, p. 105782P
2018
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2018
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K. A. Eppenhof, M. W. Lafarge, P. Moeskops, M. Veta, and J. P. Pluim, “Deformable image registration using convolutional neural networks,” in Medical Imaging 2018: Image Processing , vol. 10574. International Society for Optics and Photonics, 2018, p. 105740S
2018
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J. Zheng, S. Miao, Z. J. Wang, and R. Liao, “Pairwise domain adaptation module for cnn-based 2-d/3-d registration,” Journal of Medical Imaging , vol. 5, no. 2, p. 021204, 2018
2018
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J. Lv, M. Yang, J. Zhang, and X. Wang, “Respiratory motion correction for free-breathing 3d abdominal mri using cnn-based image registration: a feasibility study,” The British journal of radiology , vol. 91, no. xxxx, p. 20170788, 2018
2018
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J. Lv, W. Huang, J. Zhang, and X. Wang, “Performance of u-net based pyramidal lucas-kanade registration on free-breathing multi-b-value diffusion mri of the kidney,” The British journal of radiology , vol. 91, no. 1086, p. 20170813, 2018
2018
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2018
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Y. Hu, M. Modat, E. Gibson, N. Ghavami, E. Bonmati, C. M. Moore, M. Emberton, J. A. Noble, D. C. Barratt, and T. Vercauteren, “Label-driven weakly-supervised learning for multimodal deformarle image registration,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 1070–1074
2018
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2018
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T. Elss, H. Nickisch, T. Wissel, R. Bippus, M. Morlock, and M. Grass, “Motion estimation in coronary ct angiography images using convolutional neural networks,” 2018
2018
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2018
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W. Yan, H. Zhang, J. Sui, and D. Shen, “Deep chronnectome learning via full bidirectional long short-term memory networks for mci diagnosis,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 249–257
2018
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A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz, and F. Meneguzzi, “Identification of autism spectrum disorder using deep learning and the abide dataset,” NeuroImage: Clinical , vol. 17, pp. 16–23, 2018
2018
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2018
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2018
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R. Awan, N. A. Koohbanani, M. Shaban, A. Lisowska, and N. Rajpoot, “Context-aware learning using transferable features for classification of breast cancer histology images,” in International Conference Image Analysis and Recognition . Springer, 2018, pp. 788–795
2018
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2018
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M. A. Troester, X. Sun, E. H. Allott, J. Geradts, S. M. Cohen, C.-K. Tse, E. L. Kirk, L. B. Thorne, M. Mathews, Y. Li et al. , “Racial differences in pam50 subtypes in the carolina breast cancer study,” JNCI: Journal of the National Cancer Institute , vol. 110, no. 2, 2018
2018
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N. Antropova, H. Abe, and M. L. Giger, “Use of clinical mri maximum intensity projections for improved breast lesion classification with deep convolutional neural networks,” Journal of Medical Imaging , vol. 5, no. 1, p. 014503, 2018
2018
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D. Ribli, A. Horváth, Z. Unger, P. Pollner, and I. Csabai, “Detecting and classifying lesions in mammograms with deep learning,” Scientific reports , vol. 8, no. 1, p. 4165, 2018
2018
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Y. Zheng, C. Yang, and A. Merkulov, “Breast cancer screening using convolutional neural network and follow-up digital mammography,” in Computational Imaging III , vol. 10669. International Society for Optics and Photonics, 2018, p. 1066905
2018
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M. S. Ayhan and P. Berens, “Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks,” 2018
2018
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R. Dey, Z. Lu, and Y. Hong, “Diagnostic classification of lung nodules using 3d neural networks,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 774–778
2018
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M. Gao, U. Bagci, L. Lu, A. Wu, M. Buty, H.-C. Shin, H. Roth, G. Z. Papadakis, A. Depeursinge, R. M. Summers et al. , “Holistic classification of ct attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization , vol. 6, no. 1, pp. 1–6, 2018
2018
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C. Biffi, O. Oktay, G. Tarroni, W. Bai, A. De Marvao, G. Doumou, M. Rajchl, R. Bedair, S. Prasad, S. Cook et al. , “Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 464–471
2018
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C. Brestel, R. Shadmi, I. Tamir, M. Cohen-Sfaty, and E. Elnekave, “Radbot-cxr: Classification of four clinical finding categories in chest x-ray using deep learning,” 2018
2018
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N. Coudray, P. S. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Fenyö, A. L. Moreira, N. Razavian, and A. Tsirigos, “Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning,” Nature medicine , vol. 24, no. 10, p. 1559, 2018
2018
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J. M. Tomczak, M. Ilse, M. Welling, M. Jansen, H. G. Coleman, M. Lucas, K. de Laat, M. de Bruin, H. Marquering, M. J. van der Wel et al. , “Histopathological classification of precursor lesions of esophageal adenocarcinoma: A deep multiple instance learning approach,” 2018
2018
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2018
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M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Synthetic data augmentation using gan for improved liver lesion classification,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 289–293
2018
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2018
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M. Combalia and V. Vilaplana, “Monte-carlo sampling applied to multiple instance learning for whole slide image classification,” 2018
2018
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O. Paserin, K. Mulpuri, A. Cooper, A. J. Hodgson, and R. Garbi, “Real time rnn based 3d ultrasound scan adequacy for developmental dysplasia of the hip,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 365–373
2018
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A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese, “Taskonomy: Disentangling task transfer learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3712–3722
2018
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H. Haenssle, C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, A. Blum, A. Kalloo, A. B. H. Hassen, L. Thomas, A. Enk et al. , “Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists,” Annals of Oncology , vol. 29, no. 8, pp. 1836–1842, 2018
2018
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A. Panda, T. K. Mishra, and V. G. Phaniharam, “Automated brain tumor detection using discriminative clustering based mri segmentation,” in Smart Innovations in Communication and Computational Sciences . Springer, 2019, pp. 117–126
2019
Closest in time.
K. R. Laukamp, F. Thiele, G. Shakirin, D. Zopfs, A. Faymonville, M. Timmer, D. Maintz, M. Perkuhn, and J. Borggrefe, “Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric mri,” European radiology , vol. 29, no. 1, pp. 124–132, 2019
2019
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T.-C. Chiang, Y.-S. Huang, R.-T. Chen, C.-S. Huang, and R.-F. Chang, “Tumor detection in automated breast ultrasound using 3-d cnn and prioritized candidate aggregation,” IEEE Transactions on Medical Imaging , vol. 38, no. 1, pp. 240–249, 2019
2019
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F. Li, H. Chen, Z. Liu, X. Zhang, and Z. Wu, “Fully automated detection of retinal disorders by image-based deep learning,” Graefe’s Archive for Clinical and Experimental Ophthalmology , pp. 1–11, 2019
2019
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P. Khojasteh, L. A. P. Júnior, T. Carvalho, E. Rezende, B. Aliahmad, J. P. Papa, and D. K. Kumar, “Exudate detection in fundus images using deeply-learnable features,” Computers in biology and medicine , vol. 104, pp. 62–69, 2019
2019
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F.-C. Ghesu, B. Georgescu, Y. Zheng, S. Grbic, A. Maier, J. Hornegger, and D. Comaniciu, “Multi-scale deep reinforcement learning for real-time 3d-landmark detection in ct scans,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 1, pp. 176–189, 2019
2019
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Y. Horie, T. Yoshio, K. Aoyama, S. Yoshimizu, Y. Horiuchi, A. Ishiyama, T. Hirasawa, T. Tsuchida, T. Ozawa, S. Ishihara et al. , “Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks,” Gastrointestinal endoscopy , vol. 89, no. 1, pp. 25–32, 2019
2019
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T. J. Jebaseeli, C. A. D. Durai, and J. D. Peter, “Segmentation of retinal blood vessels from ophthalmologic diabetic retinopathy images,” Computers & Electrical Engineering , vol. 73, pp. 245–258, 2019
2019
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X. Liu, J. Cao, T. Fu, Z. Pan, W. Hu, K. Zhang, and J. Liu, “Semi-supervised automatic segmentation of layer and fluid region in retinal optical coherence tomography images using adversarial learning,” IEEE Access , vol. 7, pp. 3046–3061, 2019
2019
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B. D. de Vos, F. F. Berendsen, M. A. Viergever, H. Sokooti, M. Staring, and I. Išgum, “A deep learning framework for unsupervised affine and deformable image registration,” Medical image analysis , vol. 52, pp. 128–143, 2019
2019
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P. Sudharshan, C. Petitjean, F. Spanhol, L. E. Oliveira, L. Heutte, and P. Honeine, “Multiple instance learning for histopathological breast cancer image classification,” Expert Systems with Applications , vol. 117, pp. 103–111, 2019
2019
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K. Roy, D. Banik, D. Bhattacharjee, and M. Nasipuri, “Patch-based system for classification of breast histology images using deep learning,” Computerized Medical Imaging and Graphics , vol. 71, pp. 90–103, 2019
2019
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M. Mateen, J. Wen, S. Song, Z. Huang et al. , “Fundus image classification using vgg-19 architecture with pca and svd,” Symmetry , vol. 11, no. 1, p. 1, 2019
2019
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