Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Eugene Vorontsov"'
Autor:
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, Henkjan Huisman, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Golia Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbeláez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Ildoo Kim, Klaus Maier-Hein, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well o
Externí odkaz:
https://doaj.org/article/b2b75489b22a48efa4cd6eef2bea9d67
Autor:
William Trung Le, Eugene Vorontsov, Francisco Perdigón Romero, Lotfi Seddik, Mohamed Mortada Elsharief, Phuc Felix Nguyen-Tan, David Roberge, Houda Bahig, Samuel Kadoury
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-17 (2022)
Abstract In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life.
Externí odkaz:
https://doaj.org/article/552bbf2d6e5040d5bac962f8439f709d
Publikováno v:
Journal of Applied Clinical Medical Physics. 23
External radiation therapy planning is a highly complex and tedious process as it involves treating large target volumes, prescribing several levels of doses, as well as avoiding irradiating critical structures such as organs at risk close to the tum
Autor:
Eugene Vorontsov, Pavlo Molchanov, Matej Gazda, Christopher Beckham, Jan Kautz, Samuel Kadoury
Publikováno v:
Medical image analysis. 82
An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labe
Autor:
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yue, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
Publikováno v:
Med. Image Anal. 84:102680 (2022)
Medical Image Analysis, 84
Bilic, P, Christ, P, Li, H B, Vorontsov, E, Ben-Cohen, A, Kaissis, G, Szeskin, A, Jacobs, C, Mamani, G E H, Chartrand, G, Lohöfer, F, Holch, J W, Sommer, W, Hofmann, F, Hostettler, A, Lev-Cohain, N, Drozdzal, M, Amitai, M M, Vivanti, R, Sosna, J, Ezhov, I, Sekuboyina, A, Navarro, F, Kofler, F, Paetzold, J C, Shit, S, Hu, X, Lipková, J, Rempfler, M, Piraud, M, Kirschke, J, Wiestler, B, Zhang, Z, Hülsemeyer, C, Beetz, M, Ettlinger, F, Antonelli, M, Bae, W, Bellver, M, Bi, L, Chen, H, Chlebus, G, Dam, E B, Dou, Q, Fu, C-W, Georgescu, B, Giró-I-Nieto, X, Gruen, F, Han, X, Heng, P-A, Hesser, J, Moltz, J H, Igel, C, Isensee, F, Jäger, P, Jia, F, Kaluva, K C, Khened, M, Kim, I, Kim, J-H, Kim, S, Kohl, S, Konopczynski, T, Kori, A, Krishnamurthi, G, Li, F, Li, H, Li, J, Li, X, Lowengrub, J, Ma, J, Maier-Hein, K, Maninis, K-K, Meine, H, Merhof, D, Pai, A, Perslev, M, Petersen, J, Pont-Tuset, J, Qi, J, Qi, X, Rippel, O, Roth, K, Sarasua, I, Schenk, A, Shen, Z, Torres, J, Wachinger, C, Wang, C, Weninger, L, Wu, J, Xu, D, Yang, X, Yu, S C-H, Yuan, Y, Yue, M, Zhang, L, Cardoso, J, Bakas, S, Braren, R, Heinemann, V, Pal, C, Tang, A, Kadoury, S, Soler, L, van Ginneken, B, Greenspan, H, Joskowicz, L & Menze, B 2023, ' The Liver Tumor Segmentation Benchmark (LiTS) ', Medical Image Analysis, vol. 84, 102680 . https://doi.org/10.1016/j.media.2022.102680
Medical Image Analysis, 84
Bilic, P, Christ, P, Li, H B, Vorontsov, E, Ben-Cohen, A, Kaissis, G, Szeskin, A, Jacobs, C, Mamani, G E H, Chartrand, G, Lohöfer, F, Holch, J W, Sommer, W, Hofmann, F, Hostettler, A, Lev-Cohain, N, Drozdzal, M, Amitai, M M, Vivanti, R, Sosna, J, Ezhov, I, Sekuboyina, A, Navarro, F, Kofler, F, Paetzold, J C, Shit, S, Hu, X, Lipková, J, Rempfler, M, Piraud, M, Kirschke, J, Wiestler, B, Zhang, Z, Hülsemeyer, C, Beetz, M, Ettlinger, F, Antonelli, M, Bae, W, Bellver, M, Bi, L, Chen, H, Chlebus, G, Dam, E B, Dou, Q, Fu, C-W, Georgescu, B, Giró-I-Nieto, X, Gruen, F, Han, X, Heng, P-A, Hesser, J, Moltz, J H, Igel, C, Isensee, F, Jäger, P, Jia, F, Kaluva, K C, Khened, M, Kim, I, Kim, J-H, Kim, S, Kohl, S, Konopczynski, T, Kori, A, Krishnamurthi, G, Li, F, Li, H, Li, J, Li, X, Lowengrub, J, Ma, J, Maier-Hein, K, Maninis, K-K, Meine, H, Merhof, D, Pai, A, Perslev, M, Petersen, J, Pont-Tuset, J, Qi, J, Qi, X, Rippel, O, Roth, K, Sarasua, I, Schenk, A, Shen, Z, Torres, J, Wachinger, C, Wang, C, Weninger, L, Wu, J, Xu, D, Yang, X, Yu, S C-H, Yuan, Y, Yue, M, Zhang, L, Cardoso, J, Bakas, S, Braren, R, Heinemann, V, Pal, C, Tang, A, Kadoury, S, Soler, L, van Ginneken, B, Greenspan, H, Joskowicz, L & Menze, B 2023, ' The Liver Tumor Segmentation Benchmark (LiTS) ', Medical Image Analysis, vol. 84, 102680 . https://doi.org/10.1016/j.media.2022.102680
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e22b7afec9cac0b49d2cb615d2787280
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=67003
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=67003
Autor:
Matthew G Hanna, Patricia Raciti, Alican Bozkurt, Ran Godrich, Julian Viret, Donghun Lee, Philippe Mathieu, Matthew Lee, Eugene Vorontsov, Tomer Sabo, Felipe C Geyer, Jorge S Reis-Filho, Leo Grady, Thomas Fuchs, Christopher Kanan
Publikováno v:
Cancer Research. 82:PD11-02
Background. The female mammary gland can develop a myriad of epithelial proliferative lesions including, high risk lesions, in-situ and invasive carcinomas. Identification of these pre-neoplastic and neoplastic conditions in biopsy specimens is cruci
Autor:
Samuel Kadoury, Eugene Vorontsov
Publikováno v:
Deep Generative Models, and Data Augmentation, Labelling, and Imperfections ISBN: 9783030882099
DGM4MICCAI/DALI@MICCAI
DGM4MICCAI/DALI@MICCAI
Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are difficult to collect and vary significantly even across expert annotators.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2533d3913b2ef126e6580ed563fc661
https://doi.org/10.1007/978-3-030-88210-5_25
https://doi.org/10.1007/978-3-030-88210-5_25
Autor:
Chris Pal, Samuel Kadoury, Simon Turcotte, An Tang, Phillip M. Cheng, Eugene Vorontsov, Gabriel Chartrand, Michal Drozdzal
Publikováno v:
RadioGraphics. 37:2113-2131
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapp
Autor:
Simpson, Amber L., Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Landman, Bennett A., Geert Litjens, Bjoern Menze, Olaf Ronneberger, Summers, Ronald M., Patrick Bilic, Christ, Patrick F., Do, Richard K. G., Marc Gollub, Jennifer Golia-Pernicka, Heckers, Stephan H., Jarnagin, William R., Mchugo, Maureen K., Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, M. Jorge Cardoso
Publikováno v:
Simpson, A L, Antonelli, M, Bakas, S, Bilello, M, Farahani, K, Ginneken, B V, 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 ' A large annotated medical image dataset for the development and evaluation of segmentation algorithms ' arXiv .
King's College London
King's College London
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3ca591376e7f91db518cd34a4b33f6d0
https://kclpure.kcl.ac.uk/portal/en/publications/a-large-annotated-medical-image-dataset-for-the-development-and-evaluation-of-segmentation-algorithms(abe938e1-ca7e-4165-bbf7-e76ecdf0d6cf).html
https://kclpure.kcl.ac.uk/portal/en/publications/a-large-annotated-medical-image-dataset-for-the-development-and-evaluation-of-segmentation-algorithms(abe938e1-ca7e-4165-bbf7-e76ecdf0d6cf).html
Publikováno v:
AAAI
Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length increases. Gradients can be attenuated by transition operators and are atte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d393bf97c1d5fe4603ca85f9d4b9421b
http://arxiv.org/abs/1902.06704
http://arxiv.org/abs/1902.06704