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International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task.
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.W., Han, X., Heng, P.A., Hesser, J., Kadoury, S., Konopczynski, T., Le, M., Li, C., Li, X., Lipkovà, J., Lowengrub, J., Meine, H., Hendrik Moltz, J., Pal, C., Piraud, M., Qi, X., Qi, J., Rempfler, M., Roth, K., Schenk, A., Sekuboyina, A., Vorontsov, E., Zhou, P., Hülsemeyer, C., Beetz, M., Ettlinger, F., Gruen, F., Kaissis, G., Lohöfer, F., Braren, R., Holch, J., Hofmann, F., Sommer, W., Heinemann, V., Jacobs, C., Efrain Humpire Mamani, G., van Ginneken, B., Chartrand , G., Tang, A., Drozdzal, M., Ben-Cohen, A., Klang, E., Amitai, M.M., Konen, E., Greenspan, H., Moreau, J., Hostettler, A., Soler, L., Vivanti, R., Szeskin, A., Lev-Cohain, N., Sosna, J., Joskowicz, L., Menze, B.H., 2019 · 1901
Earlier work this paper cites.
Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., Bilic, P., Christ, P.F., Do, R.K.G., Gollub, M., Golia-Pernicka, J., Heckers, S.H., Jarnagin, W.R., McHugo, M.K., Napel, S., Vorontsov, E., Maier-Hein, L., Cardoso, M.J., 2019 · 1902
Earlier work this paper cites.
A new measure of rank correlation
Kendall, M.G., 1938 · 1938
Earlier work this paper cites.
Measures of the amount of ecologic association between species
Dice, L.R., 1945 · 1945
Earlier work this paper cites.
Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning
Liang, S., Tang, F., Huang, X., Yang, K., Zhong, T., Hu, R., Liu, S., Yuan, X., Zhang, Y., 2019 · 1967
Earlier work this paper cites.
Approximate inference in generalized linear mixed models
Breslow, N.E., Clayton, D.G., 1993 · 1993
Earlier work this paper cites.
Solving large scale linear prediction problems using stochastic gradient descent algorithms, in: Proceedings of the twenty-first international conference on Machine learning, p. 116
Zhang, T., 2004 · 2004
Earlier work this paper cites.
Automl segmentation for 3d medical image data: Contribution to the msd challenge 2018
Rippel, O., Weninger, L., Merhof, D., 2020 · 2005
Earlier work this paper cites.
Liver segmentation from computed tomography scans: a survey and a new algorithm
Campadelli, P., Casiraghi, E., Esposito, A., 2009 · 2009
Earlier work this paper cites.
Robust texture features for response monitoring of glioblastoma multiforme on-weighted and-flair mr images: A preliminary investigation in terms of identification and segmentation
Assefa, D., Keller, H., Ménard, C., Laperriere, N., Ferrari, R.J., Yeung, I., 2010 · 2010
Earlier work this paper cites.
Enhancing pancreatic adenocarcinoma delineation in diffusion derived intravoxel incoherent motion f-maps through automatic vessel and duct segmentation
Re, T.J., Lemke, A., Klauss, M., Laun, F.B., Simon, D., Grünberg, K., Delorme, S., Grenacher, L., Manfredi, R., Mucelli, R.P., et al., 2011 · 2011
Earlier work this paper cites.
The multimodal brain tumor image segmentation benchmark (brats)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., et al., 2015 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
Benchmark for algorithms segmenting the left atrium from 3d ct and mri datasets
Tobon-Gomez, C., Geers, A.J., Peters, J., Weese, J., Pinto, K., Karim, R., Ammar, M., Daoudi, A., Margeta, J., Sandoval, Z., Stender, B., Zheng, Y., Zuluaga, M.A., Betancur, J., Ayache, N., Chikh, M.A., Dillenseger, J., Kelm, B.M., Mahmoudi, S., Ourselin, S., Schlaefer, A., Schaeffter, T., Razavi, R., Rhode, K.S., 2015 · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Cited alongside, same era.
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C.I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen-Van De Kaa, C., Bult, P., Van Ginneken, B., Van Der Laak, J., 2016 · 2016
Cited alongside, same era.
20th anniversary of the medical image analysis journal (media)
Ayache, N., Duncan, J., 2016 · 2016
Cited alongside, same era.
Quicknat: Segmenting MRI neuroanatomy in 20 seconds
Roy, A.G., Conjeti, S., Navab, N., Wachinger, C., 2018 · 2018
Later among the works it cites.
Nested dilation network (ndn) for multi-task medical image segmentation
Wang, L., Chen, R., Wang, S., Zeng, N., Huang, X., Liu, C., 2019 · 2018
Later among the works it cites.
Inter-observer variability of manual contour delineation of structures in ct
Joskowicz, L., Cohen, D., Caplan, N., Sosna, J., 2019 · 2019
Later among the works it cites.
Neural architecture search: A survey
Elsken, T., Metzen, J.H., Hutter, F., et al., 2019 · 2019
Later among the works it cites.
One network to segment them all: A general, lightweight system for accurate 3d medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 30–38
Perslev, M., Dam, E.B., Pai, A., Igel, C., 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Deepmedic for brain tumor segmentation, in: International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, Springer. pp. 138–149
Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A.V., Criminisi, A., Rueckert, D., Glocker, B., 2016 · 2016
Cited alongside, same era.
Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C., 2017 · 2017
Cited alongside, same era.
Gland segmentation in colon histology images: The glas challenge contest
Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al., 2017 · 2017
Cited alongside, same era.
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L., Webster, D.R., 2018 · 2018
Cited alongside, same era.
Why rankings of biomedical image analysis competitions should be interpreted with care
Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A.P., Carass, A., Feldmann, C., Frangi, A.F., Full, P.M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B.A., März, K., Maier, O., Maier-Hein, K., Menze, B.H., Müller, H., Neher, P.F., Niessen, W., Rajpoot, N., Sharp, G.C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Taha, A.A., van der Sommen, F., Wang, C.W., Weber, M.A., Zheng, G., Jannin, P., Kopp-Schneider, A., 2018 · 2018
Cited alongside, same era.
Why rankings of biomedical image analysis competitions should be interpreted with care
Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A.P., Carass, A., Feldmann, C., Frangi, A.F., Full, P.M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B.A., März, K., Maier, O., Maier-Hein, K., Menze, B.H., Müller, H., Neher, P.F., Niessen, W., Rajpoot, N., Sharp, G.C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Taha, A.A., van der Sommen, F., Wang, C.W., Weber, M.A., Zheng, G., Jannin, P., Kopp-Schneider, A., 2018 · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
A deep learning approach to antibiotic discovery
Stokes, J.M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N.M., MacNair, C.R., French, S., Carfrae, L.A., Bloom-Ackermann, Z., et al., 2020 · 2020
Later among the works it cites.
Bias: Transparent reporting of biomedical image analysis challenges
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A.L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., et al., 2020 · 2020
Later among the works it cites.
Cutting-edge 3d medical image segmentation methods in 2020: Are happy families all alike?
Ma, J., 2021 · 2020
Later among the works it cites.
3d semi-supervised learning with uncertainty-aware multi-view co-training, in: The IEEE Winter Conference on Applications of Computer Vision, pp. 3646–3655
Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., Roth, H., 2020 · 2020
Later among the works it cites.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
Closest in time.
Methods and open-source toolkit for analyzing and visualizing challenge results
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Saiz, L.A., Cardoso, M.J., Maier-Hein, L., Kopp-Schneider, A., 2021 · 2021
Closest in time.
Common limitations of image processing metrics: A picture story
Reinke, A., Eisenmann, M., Tizabi, M.D., Sudre, C.H., Rädsch, T., Antonelli, M., Arbel, T., Bakas, S., Cardoso, M.J., Cheplygina, V., et al., 2021 · 2021
Closest in time.
Revisiting resnets: Improved training and scaling strategies
Bello, I., Fedus, W., Du, X., Cubuk, E.D., Srinivas, A., Lin, T.Y., Shlens, J., Zoph, B., 2021 · 2021
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