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Deep learning models for semantic segmentation of images require large amounts of data.
French, R.M.: Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences 3, 128-135 (1999)
1999
Earlier work this paper cites.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Ç, D., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R., Reza, S.M.S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Leemput, K.V.: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging 34
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv e-prints, (2015)
2015
Earlier work this paper cites.
Shokri, R., Smatikov, V.: Privacy-Preserving Deep Learning. CCS ’15 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1310-1321 (2015)
2015
Earlier work this paper cites.
Akbari, H., Macyszyn, L., Da, X., Bilello, M., Wolf, R.L., Martinez-Lage, M., Biros, G., Alonso-Basanta, M., O’Rourke, D.M., Davatzikos, C.: Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma. Neurosurgery 78
2016
Earlier work this paper cites.
Macyszyn, L., Akbari, H., Pisapia, J.M., Da, X., Attiah, M., Pigrish, V., Bi, Y., Pal, S., Davuluri, R.V., Roccograndi, L., Dahmane, N., Martinez-Lage, M., Biros, G., Wolf, R.L., Bilello, M., O’Rourke, D.M., Davatzikos, C.: Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-Oncology 18
2016
Earlier work this paper cites.
Tresp, V., Overhage, J.M., Bundschus, M., Rabizadeh, S., Fasching, P.A., Yu, S.: Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare. Proceedings of the IEEE 104, 2180-2206 (2016)
2016
Earlier work this paper cites.
Brendan McMahan, H., Moore, E., Ramage, D., Hampson, S., Agüera y Arcas, B.: Communication-Efficient Learning of Deep Networks from Decentralized Data. ArXiv e-prints, (2016)
2016
Earlier work this paper cites.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep Learning with Differential Privacy. CCS ’16 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 308-318 (2016)
2016
Cited alongside, same era.
Bakas, S., Akbari, H., Pisapia, J., Martinez-Lage, M., Rozycki, M., Rathore, S., Dahmane, N., O’Rourke, D.M., Davatzikos, C.: In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the ϕ \phi -index. Clinical Cancer Research 23
2017
Cited alongside, same era.
Korfiatis, P., Kline, T.L., Lachance, D.H., Parney, I.F., Buckner, J.C., Erickson, B.J.: Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. Journal of Digital Imaging 30
2017
Cited alongside, same era.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nature Scientific Data 4
Brendan McMahan, H., Ramage, D., Talwar, K., Zhang, L.: Learning Differentially Private Recurrent Language Models. ArXiv e-prints, (2017)
2017
Later among the works it cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences 114, 3521-3526 (2017)
2017
Later among the works it cites.
Chang, K., Bai, H.X., Zhou, H., Su, C., Bi, W.L., Agbodza, E., Kavouridis, V.K., Senders, J.T., Boaro, A., Beers, A., Zhang, B., Capellini, A., Liao, W., Shen, Q., Li, X., Xiao, B., Cryan, J., Ramkissoon, S., Ramkissoon, L., Ligon, K., Wen, P.Y., Bindra, R.S., Woo, J., Arnaout, O., Gerstner, E.R., Zhang, P.J., Rosen, B.R., Yang, L., Huang, R.Y., Kalpathy-Cramer, J.: Residual Convolutional Neural Network for the Determination of ¡em¿IDH¡/em¿ Status in Low- and High-Grade Gliomas from MR Imaging. Clinical Cancer Research 24
2018
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2017
Cited alongside, same era.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Davatzikos, C.: Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection. In: The Cancer Imaging Archive, (2017)
2017
Cited alongside, same era.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Davatzikos, C.: Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection. In: The Cancer Imaging Archive, (2017)
2017
Cited alongside, same era.
Chen, M., Qian, Y., Chen, J., Hwang, K., Mao, S., Hu, L.: Privacy Protection and Intrusion Avoidance for Cloudlet-based Medical Data Sharing. IEEE Transactions on Cloud Computing 1-1 (2017)
2017
Cited alongside, same era.
Geyer, R.C., Klein, T., Nabi, M.: Differentially Private Federated Learning: A Client Level Perspective. ArXiv e-prints, (2017)
2017
Cited alongside, same era.
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D.L., Kalpathy-Cramer, J.: Distributed deep learning networks among institutions for medical imaging. Journal of the American Medical Informatics Association ocy017-ocy017 (2018)
2018
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Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How To Backdoor Federated Learning. ArXiv e-prints, (2018)
2018
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Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated Learning with Non-IID Data. ArXiv e-prints, (2018)
2018
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Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis 43, 98-111 (2018)
2018
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