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Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions.
Design patterns: elements of reusable object-oriented software
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The Medical Imaging Interaction Toolkit: challenges and advances
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Attenuation correction synthesis for hybrid PET-MR scanners: Application to brain studies
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A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations, in: MICCAI
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The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging
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Lasagne: First release
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NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics
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GIFT-Cloud: A data sharing and collaboration platform for medical imaging research
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TensorFlow: Large-scale machine learning on heterogeneous distributed systems
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3D U-net: learning dense volumetric segmentation from sparse annotation, in: MICCAI, Springer. pp. 424–432
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Goodfellow, I., 2016 · 2016
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V-Net: Fully convolutional neural networks for volumetric medical image segmentation, in: Proceedings of the Fourth International Conference on 3D Vision (3DV’16), pp. 565–571
Milletari, F., Navab, N., Ahmadi, S.A., 2016 · 2016
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Data from TCIA Pancreas-CT
Roth, H.R., Farag, A., Turkbey, E.B., Lu, L., Liu, J., Summers, R.M., 2016 · 2016
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Gibson, E., Robu, M.R., Thompson, S., Edwards, P.E., Schneider, C., Gurusamy, K., Davidson, B., Hawkes, D.J., Barratt, D.C., Clarkson, M.J., 2017c · 2017
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Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks, in: Proceedings of MICCAI’17 Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO’17)
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B., 2017 · 2017
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On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task, in: Proceedings of Information Processing in Medical Imaging (IPMI’17), pp. 348–360
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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.W.M., van Ginneken, B., Sánchez, C.I., 2017 · 2017
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DeepInfer: Open-source deep learning deployment toolkit for image-guided therapy, in: Proceedings of the SPIE, Medical Imaging 2017, NIH Public Access
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Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations, in: Proceedings of MICCAI’17 Workshop on Deep Learning in Medical Image Analysis (DLMIA’17)
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The multimodal brain tumor image segmentation benchmark (BraTS)
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. Demiralp, 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., 2015 · 2024
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A reproducible evaluation of ANTs similarity metric performance in brain image registration
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C., 2011 · 2044
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