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Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
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Objective criteria for the evaluation of clustering methods
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: · 2011
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Diverse m-best solutions in markov random fields
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Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2013
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The cancer imaging archive (tcia): maintaining and operating a public information repository
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Stochastic backpropagation and approximate inference in deep generative models
Jimenez Rezende, D., Mohamed, S., Wierstra, D.: · 2014
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Semi-supervised learning with deep generative models
Kingma, D.P., Jimenez Rezende, D., Mohamed, S., Welling, M.: · 2014
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Graph-based active learning of agglomeration (gala): a python library to segment 2d and 3d neuroimages
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Kendall, A., Badrinarayanan, V., Cipolla, R.: · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., Yan, X.: · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Saturated reconstruction of a volume of neocortex
Kasthuri, N., Hayworth, K.J., Berger, D.R., Schalek, R.L., Conchello, J.A., Knowles-Barley, S., Lee, D., Vázquez-Reina, A., Kaynig, V., Jones, T.R., et al.: · 2015
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Data from lidc-idri. the cancer imaging archive
Armato, I., Samuel, G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Clarke, L.P.: · 2015
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Crowdsourcing the creation of image segmentation algorithms for connectomics
Arganda-Carreras, I., Turaga, S.C., Berger, D.R., Cireşan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J., Laptev, D., Dwivedi, S., Buhmann, J.M., et al.: · 2015
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Stochastic multiple choice learning for training diverse deep ensembles
Lee, S., Prakash, S.P.S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: · 2016
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Towards conceptual compression
Gregor, K., Besse, F., Rezende, D.J., Danihelka, I., Wierstra, D.: · 2016
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Ladder variational autoencoders
Sønderby, C.K., Raiko, T., Maaløe, L., Sønderby, S.K., Winther, O.: · 2016
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Improving variational inference with inverse autoregressive flow.(nips), 2016
Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Bridging category-level and instance-level semantic image segmentation
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
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Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al.: · 2018
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A variational u-net for conditional appearance and shape generation
Esser, P., Sutter, E., Ommer, B.: · 2018
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A probabilistic u-net for segmentation of ambiguous images
Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J.R., Maier-Hein, K., Eslami, S.A., Rezende, D.J., Ronneberger, O.: · 2018
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., Poole, B.: · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A., Gal, Y.: · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., Blundell, C.: · 2017
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Learning in an uncertain world: Representing ambiguity through multiple hypotheses
Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., Hager, G.D.: · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
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Menick, J., Kalchbrenner, N.: · 2018
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Rezende, D.J., Viola, F.: · 2018
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Nikolov, S., Blackwell, S., Mendes, R., De Fauw, J., Meyer, C., Hughes, C., Askham, H., Romera-Paredes, B., Karthikesalingam, A., Chu, C., et al.: · 2018
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Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: · 2018
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Instance segmentation by deep coloring
Kulikov, V., Yurchenko, V., Lempitsky, V.: · 2018
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y.W., Gorur, D., Lakshminarayanan, B.: · 2018
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Semantic image synthesis with spatially-adaptive normalization
Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: · 2019
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Biva: A very deep hierarchy of latent variables for generative modeling
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Instance segmentation of biological images using harmonic embeddings
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Upsnet: A unified panoptic segmentation network
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Tensormask: A foundation for dense object segmentation
Chen, X., Girshick, R., He, K., Dollár, P.: · 2019
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