Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Avinash Kori"'
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Whole-slide imaging (WSI), i.e., the scanning and digitization of entire histology slides, are now being adopted across the world in pathology
Externí odkaz:
https://doaj.org/article/41e5df922c4a416987a654bf7f568fc6
Publikováno v:
Frontiers in Computational Neuroscience, Vol 15 (2021)
Externí odkaz:
https://doaj.org/article/e9255fe12a494d77aec180672f74fe8b
Publikováno v:
Frontiers in Computational Neuroscience, Vol 14 (2020)
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segm
Externí odkaz:
https://doaj.org/article/cf7ecc8787ac4f1d88c8431a4a7c2075
Autor:
Tahsin Kurc, Spyridon Bakas, Xuhua Ren, Aditya Bagari, Alexandre Momeni, Yue Huang, Lichi Zhang, Ashish Kumar, Marc Thibault, Qi Qi, Qian Wang, Avinash Kori, Olivier Gevaert, Yunlong Zhang, Dinggang Shen, Mahendra Khened, Xinghao Ding, Ganapathy Krishnamurthi, Jayashree Kalpathy-Cramer, James Davis, Tianhao Zhao, Rajarsi Gupta, Joel Saltz, Keyvan Farahani
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and clinical data.
Externí odkaz:
https://doaj.org/article/0c2926c1e89042aeb25319dba419c252
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164392
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::401308c8cd45cf365fe23af1f6527529
https://doi.org/10.1007/978-3-031-16440-8_63
https://doi.org/10.1007/978-3-031-16440-8_63
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
Publikováno v:
AI for Disease Surveillance and Pandemic Intelligence ISBN: 9783030930790
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b1fd0fe9ca5fc001a95b4827333e48b
https://doi.org/10.1007/978-3-030-93080-6_15
https://doi.org/10.1007/978-3-030-93080-6_15
Autor:
Oscar Esteban, Bansal S, Russell A. Poldrack, Christopher J. Markiewicz, Wazeer Zulfikar, Avinash Kori, Franklin Feingold, Joseph Wexler
Platforms and institutions that support MRI data sharing need to ensure that identifiable facial features are not present in shared images. Currently, this assessment requires manual effect as no auto-mated tools exist that can efficiently and accura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abbef0d3257dd2bf339b78c0e7b0c62b
https://doi.org/10.1101/2021.04.25.441373
https://doi.org/10.1101/2021.04.25.441373
Publikováno v:
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Vol 15 (2021)
Frontiers in Computational Neuroscience, Vol 15 (2021)
Autor:
Raghavendra S Bhat, Avinash Kori, Pranav Aurangabadkar, Ganapathy Krishnamurthi, Mahendra Khened, Sanjeev Grampurohit, Vikas Kumar Anand
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030720865
BrainLes@MICCAI (2)
BrainLes@MICCAI (2)
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connectio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fdcd9302bbc5ceda998c633b6900264
https://doi.org/10.1007/978-3-030-72087-2_27
https://doi.org/10.1007/978-3-030-72087-2_27