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Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging.
Exploring transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images, in: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE. pp. 1994–1997
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Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation
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Risks of semi-supervised learning
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Batch classification with applications in computer aided diagnosis, in: European Conference on Machine Learning (ECML). Springer, pp. 449–460
Vural, V., Fung, G., Krishnapuram, B., Dy, J., Rao, B., 2006 · 2006
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Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure, in: Computer Vision and Pattern Recognition (CVPR), IEEE. pp. 1–8
Bi, J., Liang, J., 2007 · 2007
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Multiple-instance learning algorithms for computer-aided detection
Dundar, M.M., Fung, G., Krishnapuram, B., Rao, R.B., 2007 · 2007
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Computer aided detection of pulmonary embolism with tobogganing and mutiple instance classification in CT pulmonary angiography, in: Information Processing in Medical Imaging (IPMI), Springer, Berlin, Heidelberg. pp. 630–641
Liang, J., Bi, J., 2007 · 2007
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On the relation between multi-instance learning and semi-supervised learning, in: International Conference on Machine learning (ICML), pp. 1167–1174
Zhou, Z.H., Xu, J.M., 2007 · 2007
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An Improved Multi-task Learning Approach
Bi, J., Xiong, T., Yu, S., Dundar, M., Rao, R.B., 2008 · 2008
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Learning classifiers from only positive and unlabeled data, in: International Conference on Knowledge Discovery and Data Mining, ACM. pp. 213–220
Elkan, C., Noto, K., 2008 · 2008
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Semi-supervised nasopharyngeal carcinoma lesion extraction from magnetic resonance images using online spectral clustering with a learned metric, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 51–58
Huang, W., Chan, K.L., Gao, Y., Zhou, J., Chong, V., 2008 · 2008
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Weakly supervised group-wise model learning based on discrete optimization., in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 860–868
Donner, R., Wildenauer, H., Bischof, H., Langs, G., 2009 · 2009
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Multi-level classification of emphysema in HRCT lung images
Prasad, M., Sowmya, A., Wilson, P., 2009 · 2009
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Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images
Song, Y., Zhang, C., Lee, J., Wang, F., Xiang, S., Zhang, D., 2009 · 2009
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A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis, in: Computer Vision and Pattern Recognition (CVPR), IEEE. pp. 1359–1366
Wu, D., Bi, J., Boyer, K., 2009 · 2009
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Introduction to semi-supervised learning
Zhu, X., Goldberg, A.B., 2009 · 2009
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Adversarial Pattern Classification
Biggio, B., 2010 · 2010
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Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization
Ciompi, F., Pujol, O., Gatta, C., Rodríguez-Leor, O., Mauri-Ferré, J., Radeva, P., 2010 · 2010
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A multiple instance learning approach toward optimal classification of pathology slides, in: International Conference on Pattern Recognition (ICPR), IEEE. pp. 2732–2735
Dundar, M.M., Badve, S., Raykar, V.C., Jain, R.K., Sertel, O., Gurca, M.N., 2010 · 2010
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A review of multi-instance learning assumptions
Foulds, J., Frank, E., 2010 · 2010
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Agreement-based semi-supervised learning for skull stripping., in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 147–154
Iglesias, J.E., Liu, C.Y., Thompson, P., Tu, Z., 2010 · 2010
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Lesion-specific coronary artery calcium quantification for predicting cardiac event with multiple instance support vector machines, Springer Berlin Heidelberg. pp. 484–492
Liu, Q., Qian, Z., Marvasty, I., Rinehart, S., Voros, S., Metaxas, D.N., 2010 · 2010
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A survey on transfer learning
Pan, S.J., Yang, Q., 2010 · 2010
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Multi-classifier semi-supervised classification of tuberculosis patterns on chest CT scans, in: Pulmonary Image Analysis (MICCAI PIA), pp. 41–48
van Rikxoort, E., Galperin-Aizenberg, M., Goldin, J., Kockelkorn, T.T.J.P., van Ginneken, B., Brown, M., 2010 · 2010
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Nearest neighbour group-based classification
Samsudin, N.A., Bradley, A.P., 2010 · 2010
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Boosting instance prototypes to detect local dermoscopic features
Situ, N., Yuan, X., Zouridakis, G., 2010 · 2010
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Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 666–673
Tiwari, P., Kurhanewicz, J., Rosen, M., Madabhushi, A., 2010 · 2010
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Disease classification and prediction via semi-supervised dimensionality reduction, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 1086–1090
Batmanghelich, K.N., Dong, H.Y., Pohl, K.M., Taskar, B., Davatzikos, C., et al., 2011 · 2011
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Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)
Filipovych, R., Davatzikos, C., Initiative, A.D.N., et al., 2011 · 2011
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Effective 3D object detection and regression using probabilistic segmentation features in CT images, in: Computer Vision and Pattern Recognition (CVPR), IEEE. pp. 1049–1056
Lu, L., Bi, J., Wolf, M., Salganicoff, M., 2011 · 2011
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Identifying nuclear phenotypes using semi-supervised metric learning, in: Information Processing in Medical Imaging (IPMI), Springer. pp. 398–410
Singh, S., Janoos, F., Pécot, T., Caserta, E., Leone, G., Rittscher, J., Machiraju, R., 2011 · 2011
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Learning from only positive and unlabeled data to detect lesions in vascular CT images, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 9–16
Zuluaga, M.A., Hush, D., Leyton, E.J.F.D., Hoyos, M.H., Orkisz, M., 2011 · 2011
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Transfer learning by sharing support vectors
Ablavsky, V.H., Becker, C.J., Fua, P., 2012 · 2012
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Semi-supervised segmentation using multiple segmentation hypotheses from a single atlas, in: Medical Computer Vision (MICCAI MCV), Springer. pp. 29–37
Gass, T., Székely, G., Goksel, O., 2012 · 2012
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Automated colitis detection from endoscopic biopsies as a tissue screening tool in diagnostic pathology
McCann, M.T., Bhagavatula, R., Fickus, M.C., Ozolek, J.A., Kovacevic, J., 2012 · 2012
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A multiple-instance learning framework for diabetic retinopathy screening
Quellec, G., Lamard, M., Abràmoff, M.D., Decencière, E., Lay, B., Erginay, A., Cochener, B., Cazuguel, G., 2012 · 2012
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Seeing is believing: Video classification for computed tomographic colonography using multiple-instance learning
Wang, S., McKenna, M.T., Nguyen, T.B., Burns, J.E., Petrick, N., Sahiner, B., Summers, R.M., 2012 · 2012
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Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease
Zhang, D., Shen, D., 2012 · 2012
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Multiple instance classification: Review, taxonomy and comparative study
Amores, J., 2013 · 2013
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Modeling 4D changes in pathological anatomy using domain adaptation: Analysis of TBI imaging using a tumor database
Wang, B., Prastawa, M., Saha, A., Awate, S.P., Irimia, A., Chambers, M.C., Vespa, P.M., Van Horn, J.D., Pascucci, V., Gerig, G., 2013 · 2013
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Multiple atlas construction from a heterogeneous brain MR image collection
Xie, Y., Ho, J., Vemuri, B.C., 2013 · 2013
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Automated skin biopsy histopathological image annotation using multi-instance representation and learning
Zhang, G., Yin, J., Li, Z., Su, X., Li, G., Zhang, H., 2013 · 2013
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Modeling disease progression via multi-task learning
Zhou, J., Liu, J., Narayan, V.A., Ye, J., 2013 · 2013
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Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning
Adal, K.M., Sidibé, D., Ali, S., Chaum, E., Karnowski, T.P., Mériaudeau, F., 2014 · 2014
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Domain adaptation for microscopy imaging
Becker, C., Christoudias, M., Fua, P., Christoudias, C.M., Fua, P., 2014 · 2014
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Classification of COPD with multiple instance learning, in: International Conference on Pattern Recognition (ICPR), pp. 1508–1513
Cheplygina, V., Sørensen, L., Tax, D.M.J., Pedersen, J.H., Loog, M., De Bruijne, M., 2014 · 2014
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Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut
Ciurte, A., Bresson, X., Cuisenaire, O., Houhou, N., Nedevschi, S., Thiran, J.P., Cuadra, M.B., 2014 · 2014
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A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation
Dittrich, E., Raviv, T.R., Kasprian, G., Donner, R., Brugger, P.C., Prayer, D., Langs, G., 2014 · 2014
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Generative adversarial nets, in: Advances in neural information processing systems, pp. 2672–2680
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014 · 2014
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Manifold alignment and transfer learning for classification of Alzheimer’s disease, in: Machine Learning in Medical Imaging (MICCAI MLMI). Springer. volume 8679, pp. 77–84
Guerrero, R., Ledig, C., Rueckert, D., 2014 · 2014
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Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data
Heimann, T., Mountney, P., John, M., Ionasec, R., 2014 · 2014
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Empowering multiple instance histopathology cancer diagnosis by cell graphs, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 228–235
Kandemir, M., Zhang, C., Hamprecht, F.A., 2014 · 2014
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Multiple instance learning for breast cancer magnetic resonance imaging, in: Digital lmage Computing: Techniques and Applications (DICTA), IEEE. p. 1
Maken, F.A., Gal, Y., Mcclymont, D., Bradley, A.P., 2014 · 2014
Cited alongside, same era.
Patient-specific semi-supervised learning for postoperative brain tumor segmentation., in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 714–721
Meier, R., Bauer, S., Slotboom, J., Wiest, R., Reyes, M., 2014 · 2014
Cited alongside, same era.
A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest x-rays
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R.H.H.M., Reither, K., Breuninger, M., Adetifa, I.M.O., Maane, R., Ayles, H., Sanchez, C.I., 2014 · 2014
Cited alongside, same era.
Small sample learning of superpixel classifiers for EM segmentation, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 389–397
Parag, T., Plaza, S., Scheffer, L., 2014 · 2014
Cited alongside, same era.
Learning from experts: Developing transferable deep features for patient-level lung cancer prediction, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer International Publishing, pp. 124–131
Shen, W., Zhou, M., Yang, F., Dong, D., Yang, C., Zang, Y., Tian, J., 2016 · 2016
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Transfer learning for endoscopic image classification
Sonoyama, S., Tamaki, T., Hirakawa, T., Raytchev, B., Kaneda, K., Koide, T., Yoshida, S., Mieno, H., Tanaka, S., 2016 · 2016
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Interactive cell segmentation based on active and semi-supervised learning
Su, H., Yin, Z., Huh, S., Kanade, T., Zhu, J., 2016 · 2016
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Computerized breast cancer analysis system using three stage semi-supervised learning method
Sun, W., Tseng, T.L.B., Zhang, J., Qian, W., 2016 · 2016
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Convolutional neural networks for medical image analysis: full training or fine tuning?
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Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection
Park, S.H., Gao, Y., Shi, Y., Shen, D., 2014 · 2014
Cited alongside, same era.
Unsupervised pre-training across image domains improves lung tissue classification, in: Medical Computer Vision: Algorithms for Big Data (MICCAI MCV). Springer, pp. 82–93
Schlegl, T., Ofner, J., Langs, G., 2014 · 2014
Cited alongside, same era.
Stainvas, I., Manevitch, A., Leichter, I., 2014 · 2014
Cited alongside, same era.
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Hajnal, J.V., Rueckert, D., 2014 · 2014
Cited alongside, same era.
4D active cut: An interactive tool for pathological anatomy modeling, in: International Symposium on Biomedical Imaging (ISBI), pp. 529–532
Wang, B., Liu, W., Prastawa, M., Irimia, A., Vespa, P.M., van Horn, J.D., Fletcher, P.T., Gerig, G., 2014 · 2014
Cited alongside, same era.
Weakly supervised histopathology cancer image segmentation and classification
Xu, Y., Zhu, J.Y., Chang, E.I., Lai, M., Tu, Z., 2014 · 2014
Cited alongside, same era.
Chest pathology detection using deep learning with non-medical training, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 294–297
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H., 2015 · 2015
Cited alongside, same era.
Multimodal manifold-regularized transfer learning for MCI conversion prediction
Cheng, B., Liu, M., Suk, H.I., Shen, D., Alzheimer’s Disease Neuroimaging Initiative, 2015 · 2015
Cited alongside, same era.
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J., 2016 · 2016
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Domain adaptation for Alzheimer’s disease diagnostics
Wachinger, C., Reuter, M., 2016 · 2016
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Four challenges in medical image analysis from an industrial perspective
Weese, J., Lorenz, C., 2016 · 2016
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Neuron segmentation based on CNN with semi-supervised regularization, in: Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 20–28
Xu, K., Su, H., Zhu, J., Guan, J.S., Zhang, B., 2016 · 2016
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Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition
Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D.N., Zhou, X.S., 2016 · 2016
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Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation
Alex, V., Vaidhya, K., Thirunavukkarasu, S., Kesavadas, C., Krishnamurthi, G., 2017 · 2017
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Transfer learning from rf to b-mode temporal enhanced ultrasound features for prostate cancer detection
Azizi, S., Mousavi, P., Yan, P., Tahmasebi, A., Kwak, J.T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., et al., 2017 · 2017
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Semi-supervised learning for network-based cardiac MR image segmentation, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 253–260
Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G., Glocker, B., King, A., Matthews, P.M., Rueckert, D., 2017 · 2017
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Semi-supervised deep learning for fully convolutional networks, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 311–319
Baur, C., Albarqouni, S., Navab, N., 2017 · 2017
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Multiple instance learning: A survey of problem characteristics and applications
Carbonneau, M.A., Cheplygina, V., Granger, E., Gagnon, G., 2017 · 2017
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Bladder cancer treatment response assessment using deep learning in CT with transfer learning, in: SPIE Medical Imaging, International Society for Optics and Photonics. pp. 1013404–1013404
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Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications
Chang, H., Han, J., Zhong, C., Snijders, A., Mao, J.H., 2017 · 2017
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Transfer learning for multi-center classification of chronic obstructive pulmonary disease
Cheplygina, V., Peña, I.P., Pedersen, J.H., Lynch, D.A., Sørensen, L., de Bruijne, M., 2017 · 2017
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Multisource transfer learning with convolutional neural networks for lung pattern analysis
Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., Mougiakakou, S., 2017 · 2017
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A deep learning approach for the analysis of masses in mammograms with minimal user intervention
Dhungel, N., Carneiro, G., Bradley, A.P., 2017 · 2017
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GP-Unet: Lesion detection from weak labels with a 3D regression network, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 214–221
Dubost, F., Bortsova, G., Adams, H., Ikram, A., Niessen, W., Vernooij, M., de Bruijne, M., 2017 · 2017
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Low quality dermal image classification using transfer learning, in: IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE. pp. 373–376
Elmahdy, M.S., Abdeldayem, S.S., Yassine, I.A., 2017 · 2017
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Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 516–524
Ghafoorian, M., Mehrtash, A., Kapur, T., Karssemeijer, N., Marchiori, E., Pesteie, M., Guttmann, C.R., de Leeuw, F.E., Tempany, C.M., van Ginneken, B., et al., 2017 · 2017
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Semi-supervised learning for biomedical image segmentation via forest oriented super pixels (voxels), in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer. pp. 702–710
Gu, L., Zheng, Y., Bise, R., Sato, I., Imanishi, N., Aiso, S., 2017 · 2017
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Who said what: Modeling individual labelers improves classification
Guan, M.Y., Gulshan, V., Dai, A.M., Hinton, G.E., 2017 · 2017
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Simple domain adaptation for cross-dataset analyses of brain MRI data, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 441–445
Hofer, C., Kwitt, R., Höller, Y., Trinka, E., Uhl, A., 2017 · 2017
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Towards Alzheimer’s disease classification through transfer learning
Hon, M., Khan, N., 2017 · 2017
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Epithelium-stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images
Huang, Y., Zheng, H., Liu, C., Ding, X., Rohde, G., 2017 · 2017
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Risk stratification of lung nodules using 3D CNN-based multi-task learning
Hussein, S., Cao, K., Song, Q., Bagci, U., 2017 · 2017
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Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning, in: SPIE Medical Imaging, International Society for Optics and Photonics. pp. 101340U–101340U
Huynh, B.Q., Antropova, N., Giger, M.L., 2017 · 2017
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Constrained deep weak supervision for histopathology image segmentation
Jia, Z., Huang, X., Eric, I., Chang, C., Xu, Y., 2017 · 2017
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Unsupervised domain adaptation in brain lesion segmentation with adversarial networks, in: International Conference on Information Processing in Medical Imaging (IPMI), Springer. pp. 597–609
Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., Rueckert, D., et al., 2017 · 2017
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MR acquisition-invariant representation learning
Kouw, W.M., Loog, M., Bartels, L.W., Mendrik, A.M., 2017 · 2017
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Robust semi-supervised least squares classification by implicit constraints
Krijthe, J.H., Loog, M., 2017 · 2017
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sánchez, C.I., 2017 · 2017
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Feature selection and thyroid nodule classification using transfer learning, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 1096–1099
Liu, T., Xie, S., Zhang, Y., Yu, J., Niu, L., Sun, W., 2017 · 2017
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Multiple instance learning for age-related macular degeneration diagnosis in optical coherence tomography images, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 139–142
Lu, D., Ding, W., Merkur, A., Sarunic, M.V., Beg, M.F., 2017 · 2017
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Unsupervised reverse domain adaption for synthetic medical images via adversarial training
Mahmood, F., Chen, R., Durr, N.J., 2017 · 2017
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Knowledge transfer for melanoma screening with deep learning
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., Valle, E., 2017 · 2017
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Liver fibrosis classification based on transfer learning and fcnet for ultrasound images
Meng, D., Zhang, L., Cao, G., Cao, W., Zhang, G., Hu, B., 2017 · 2017
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Murphree, D.H., Ngufor, C., 2017 · 2017
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Center-focusing multi-task CNN with injected features for classification of glioma nuclear images, in: IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. pp. 834–841
Murthy, V., Hou, L., Samaras, D., Kurc, T.M., Saltz, J.H., 2017 · 2017
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Multiple-instance learning for medical image and video analysis
Quellec, G., Cazuguel, G., Cochener, B., Lamard, M., 2017 · 2017
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Exploring texture transfer learning for colonic polyp classification via convolutional neural networks, in: International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 1044–1048
Ribeiro, E., Häfner, M., Wimmer, G., Tamaki, T., Tischendorf, J., Yoshida, S., Tanaka, S., Uhl, A., 2017 · 2017
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Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Ross, T., Zimmerer, D., Vemuri, A., Isensee, F., Bodenstedt, S., Both, F., Kessler, P., Wagner, M., Müller, B., Kenngott, H., et al., 2017 · 2017
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An overview of multi-task learning in deep neural networks
Ruder, S., 2017 · 2017
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Shin, S.Y., Lee, S., Yun, I.D., Lee, K.M., 2017 · 2017
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Deep features for breast cancer histopathological image classification, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE. pp. 1868–1873
Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L., 2017 · 2017
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Deep learning with permutation-invariant operator for multi-instance histopathology classification
Tomczak, J.M., Ilse, M., Welling, M., 2017 · 2017
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Direct aneurysm volume estimation by multi-view semi-supervised manifold learning, in: International Symposium on Biomedical Imaging, IEEE. pp. 1222–1225
Wang, L., Li, S., Chen, Y., Lin, J., Liu, C., Zeng, X., Li, S., 2017 · 2017
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Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain
Zhang, R., Zheng, Y., Mak, T.W.C., Yu, R., Wong, S.H., Lau, J.Y., Poon, C.C., 2017 · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A., 2017 · 2017
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Negative results in computer vision: A perspective
Borji, A., 2018 · 2018
Closest in time.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J.M., Welling, M., 2018 · 2018
Closest in time.