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There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes.
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., et al., 2019 · 1901
Earlier work this paper cites.
Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge
Zhuang, X., Li, L., Payer, C., Stern, D., Urschler, M., Heinrich, M.P., Oster, J., Wang, C., Smedby, O., Bian, C., et al., 2019 · 1902
Earlier work this paper cites.
Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., et al., 2019b · 1904
Earlier work this paper cites.
Automated design of deep learning methods for biomedical image segmentation
Isensee, F., Jäger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2019a · 1904
Earlier work this paper cites.
nnu-net: Breaking the spell on successful medical image segmentation
Isensee, F., Petersen, J., Kohl, S.A., Jäger, P.F., Maier-Hein, K.H., 2019b · 1904
Earlier work this paper cites.
The role of publicly available data in miccai papers from 2014 to 2018
Heller, N., Rickman, J., Weight, C., Papanikolopoulos, N., 2019a · 1908
Earlier work this paper cites.
Methods and open-source toolkit for analyzing and visualizing challenge results
Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L., Kopp-Schneider, A., 2019 · 1910
Earlier work this paper cites.
The comparison of three renal tumor scoring systems: C-index, padua, and renal nephrometry scores
Okhunov, Z., Rais-Bahrami, S., George, A.K., Waingankar, N., Duty, B., Montag, S., Rosen, L., Sunday, S., Vira, M.A., Kavoussi, L.R., 2011 · 1924
Earlier work this paper cites.
Radical nephrectomy for renal cell carcinoma
Robson, C.J., 1963 · 1963
Earlier work this paper cites.
An introduction to the bootstrap
Efron, B., Tibshirani, R.J., 1994 · 1994
Earlier work this paper cites.
Comparison and evaluation of retrospective intermodality brain image registration techniques
West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer Jr, C.R., Kessler, R.M., Maciunas, R.J., Barillot, C., Lemoine, D., Collignon, A., et al., 1997 · 1997
Earlier work this paper cites.
The natural history of observed enhancing renal masses: meta-analysis and review of the world literature
Chawla, S.N., Crispen, P.L., Hanlon, A.L., Greenberg, R.E., Chen, D.Y., Uzzo, R.G., 2006 · 2006
Earlier work this paper cites.
Rising incidence of small renal masses: a need to reassess treatment effect
Hollingsworth, J.M., Miller, D.C., Daignault, S., Hollenbeck, B.K., 2006 · 2006
Earlier work this paper cites.
An image analysis approach for automatic malignancy determination of prostate pathological images
Farjam, R., Soltanian-Zadeh, H., Jafari-Khouzani, K., Zoroofi, R.A., 2007 · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Preoperative aspects and dimensions used for an anatomical (padua) classification of renal tumours in patients who are candidates for nephron-sparing surgery
Ficarra, V., Novara, G., Secco, S., Macchi, V., Porzionato, A., De Caro, R., Artibani, W., 2009 · 2009
Earlier work this paper cites.
Comparison and evaluation of methods for liver segmentation from ct datasets
Heimann, T., Van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al., 2009 · 2009
Earlier work this paper cites.
The renal nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth
Kutikov, A., Uzzo, R.G., 2009 · 2009
Earlier work this paper cites.
Kidney tumor location measurement using the c index method
Simmons, M.N., Ching, C.B., Samplaski, M.K., Park, C.H., Gill, I.S., 2010 · 2010
Earlier work this paper cites.
Renal nephrometry score predicts surgical outcomes of laparoscopic partial nephrectomy
Hayn, M.H., Schwaab, T., Underwood, W., Kim, H.L., 2011 · 2011
Earlier work this paper cites.
Anatomic features of enhancing renal masses predict malignant and high-grade pathology: a preoperative nomogram using the renal nephrometry score
Kutikov, A., Smaldone, M.C., Egleston, B.L., Manley, B.J., Canter, D.J., Simhan, J., Boorjian, S.A., Viterbo, R., Chen, D.Y., Greenberg, R.E., et al., 2011 · 2011
Earlier work this paper cites.
Su-e-t-33: pydicom: an open source dicom library
Mason, D., 2011 · 2011
Earlier work this paper cites.
Characterization of small solid renal lesions: can benign and malignant tumors be differentiated with ct?
Millet, I., Doyon, F.C., Hoa, D., Thuret, R., Merigeaud, S., Serre, I., Taourel, P., 2011 · 2011
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning, in: Proceedings of ICML workshop on unsupervised and transfer learning, pp. 17–36
Bengio, Y., 2012 · 2012
Cited alongside, same era.
Diameter-axial-polar nephrometry: integration and optimization of renal and centrality index scoring systems
Simmons, M.N., Hillyer, S.P., Lee, B.H., Fergany, A.F., Kaouk, J., Campbell, S.C., 2012 · 2012
Cited alongside, same era.
Association of prevalence of benign pathologic findings after partial nephrectomy with preoperative imaging patterns in the united states from 2007 to 2014
Kim, J.H., Li, S., Khandwala, Y., Chung, K.J., Park, H.K., Chung, B.I., 2019 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Cited alongside, same era.
Medical image file formats
Larobina, M., Murino, L., 2014 · 2014
Deep learning in medical image analysis
Shen, D., Wu, G., Suk, H.I., 2017 · 2017
Later among the works it cites.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al., 2018 · 2018
Later among the works it cites.
nipy/nibabel: 2.3. 0
Brett, M., Hanke, M., Markiewicz, C., Côté, M.A., McCarthy, P., Ghosh, S., Wassermann, D., et al., 2018 · 2018
Later among the works it cites.
nnu-net: Self-adapting framework for u-net-based medical image segmentation
Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S., et al., 2018 · 2018
Later among the works it cites.
H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A., 2018 · 2018
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Cited alongside, same era.
Microsoft coco: Common objects in context, in: European conference on computer vision, Springer. pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Cited alongside, same era.
Renal function after nephron-sparing surgery versus radical nephrectomy: results from eortc randomized trial 30904
Scosyrev, E., Messing, E.M., Sylvester, R., Campbell, S., Van Poppel, H., 2014 · 2014
Cited alongside, same era.
Isles 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri
Maier, O., Menze, B.H., von der Gablentz, J., Häni, L., Heinrich, M.P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., et al., 2017 · 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.
Interobserver variability of renal, padua, and centrality index nephrometry score systems
Spaliviero, M., Poon, B.Y., Aras, O., Di Paolo, P.L., Guglielmetti, G.B., Coleman, C.Z., Karlo, C.A., Bernstein, M.L., Sjoberg, D.D., Russo, P., et al., 2015 · 2015
Cited alongside, same era.
Renal cancer
Capitanio, U., Montorsi, F., 2016 · 2016
Cited alongside, same era.
3d u-net: learning dense volumetric segmentation from sparse annotation, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 424–432
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O., 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
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., et al., 2018 · 2018
Later among the works it cites.
Active surveillance for localized renal masses: tumor growth, delayed intervention rates, and> 5-yr clinical outcomes
McIntosh, A.G., Ristau, B.T., Ruth, K., Jennings, R., Ross, E., Smaldone, M.C., Chen, D.Y., Viterbo, R., Greenberg, R.E., Kutikov, A., et al., 2018 · 2018
Later among the works it cites.
Attention u-net: Learning where to look for the pancreas
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al., 2018 · 2018
Later among the works it cites.
Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction
Park, S.H., Han, K., 2018 · 2018
Later among the works it cites.
How to exploit weaknesses in biomedical challenge design and organization, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 388–395
Reinke, A., Eisenmann, M., Onogur, S., Stankovic, M., Scholz, P., Full, P.M., Bogunovic, H., Landman, B.A., Maier, O., Menze, B., et al., 2018 · 2018
Later among the works it cites.
Kid-net: convolution networks for kidney vessels segmentation from ct-volumes, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 463–471
Taha, A., Lo, P., Li, J., Zhao, T., 2018 · 2018
Later among the works it cites.
Semi-automatic recist labeling on ct scans with cascaded convolutional neural networks, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 405–413
Tang, Y., Harrison, A.P., Bagheri, M., Xiao, J., Summers, R.M., 2018 · 2018
Later among the works it cites.
Growth kinetics of small renal masses on active surveillance: variability and results from the dissrm registry
Uzosike, A.C., Patel, H.D., Alam, R., Schwen, Z.R., Gupta, M., Gorin, M.A., Johnson, M.H., Gausepohl, H., Riffon, M.F., Trock, B.J., et al., 2018 · 2018
Later among the works it cites.
Automatic renal nephrometry scoring using machine learning
Blake, P., Sathianathen, N., Heller, N., Rosenberg, J., Rengel, Z., Moore, K., Kaluzniak, H., Walczak, E., Papanikolopoulos, N., Weight, C., 2019 · 2019
Closest in time.
Multi-task learning for the segmentation of thoracic organs at risk in ct images., in: SegTHOR@ ISBI
He, T., Guo, J., Wang, J., Xu, X., Yi, Z., 2019 · 2019
Closest in time.
Cascaded semantic segmentation for kidney and tumor, in: Submissions to the 2019 Kidney Tumor Segmentation Challenge – KiTS19
Hou, X., Xie, C., Li, F., Nan, Y., 2019 · 2019
Closest in time.
An attempt at beating the 3d u-net, in: Submissions to the 2019 Kidney Tumor Segmentation Challenge – KiTS19
Isensee, F., Maier-Hein, K.H., 2019 · 2019
Closest in time.
Solution to the kidney tumor segmentation challenge 2019, in: Submissions to the 2019 Kidney Tumor Segmentation Challenge – KiTS19
Ma, J., 2019 · 2019
Closest in time.
Segmentation of kidney tumor by multi-resolution vb-nets, in: Submissions to the 2019 Kidney Tumor Segmentation Challenge – KiTS19
Mu, G., Lin, Z., Han, M., Yao, G., Gao, Y., 2019 · 2019
Closest in time.
Probast: a tool to assess the risk of bias and applicability of prediction model studies
Wolff, R.F., Moons, K.G., Riley, R.D., Whiting, P.F., Westwood, M., Collins, G.S., Reitsma, J.B., Kleijnen, J., Mallett, S., 2019 · 2019
Closest in time.
Cascaded volumetric convolutional network for kidney tumor segmentation from ct volumes, in: Submissions to the 2019 Kidney Tumor Segmentation Challenge – KiTS19
Zhang, Y., Wang, Y., Hou, F., Yang, J., Xiong, G., Tian, J., Zhong, C., 2019 · 2019
Closest in time.
R: A Language and Environment for Statistical Computing
R Core Team, 2020 · 2020
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