The Liver Tumor Segmentation Benchmark (LiTS)

Autor: Bilic, Patrick, Christ, Patrick, Li, Hongwei Bran, Vorontsov, Eugene, Ben-Cohen, Avi, Kaissis, Georgios, Szeskin, Adi, Jacobs, Colin, Mamani, Gabriel Efrain Humpire, Chartrand, Gabriel, Lohöfer, Fabian, Holch, Julian Walter, Sommer, Wieland, Hofmann, Felix, Hostettler, Alexandre, Lev-Cohain, Naama, Drozdzal, Michal, Amitai, Michal Marianne, Vivantik, Refael, Sosna, Jacob, Ezhov, Ivan, Sekuboyina, Anjany, Navarro, Fernando, Kofler, Florian, Paetzold, Johannes C., Shit, Suprosanna, Hu, Xiaobin, Lipková, Jana, Rempfler, Markus, Piraud, Marie, Kirschke, Jan, Wiestler, Benedikt, Zhang, Zhiheng, Hülsemeyer, Christian, Beetz, Marcel, Ettlinger, Florian, Antonelli, Michela, Bae, Woong, Bellver, Míriam, Bi, Lei, Chen, Hao, Chlebus, Grzegorz, Dam, Erik B., Dou, Qi, Fu, Chi-Wing, Georgescu, Bogdan, Giró-i-Nieto, Xavier, Gruen, Felix, Han, Xu, Heng, Pheng-Ann, Hesser, Jürgen, Moltz, Jan Hendrik, Igel, Christian, Isensee, Fabian, Jäger, Paul, Jia, Fucang, Kaluva, Krishna Chaitanya, Khened, Mahendra, Kim, Ildoo, Kim, Jae-Hun, Kim, Sungwoong, Kohl, Simon, Konopczynski, Tomasz, Kori, Avinash, Krishnamurthi, Ganapathy, Li, Fan, Li, Hongchao, Li, Junbo, Li, Xiaomeng, Lowengrub, John, Ma, Jun, Maier-Hein, Klaus, Maninis, Kevis-Kokitsi, Meine, Hans, Merhof, Dorit, Pai, Akshay, Perslev, Mathias, Petersen, Jens, Pont-Tuset, Jordi, Qi, Jin, Qi, Xiaojuan, Rippel, Oliver, Roth, Karsten, Sarasua, Ignacio, Schenk, Andrea, Shen, Zengming, Torres, Jordi, Wachinger, Christian, Wang, Chunliang, Weninger, Leon, Wu, Jianrong, Xu, Daguang, Yang, Xiaoping, Yu, Simon Chun-Ho, Yuan, Yading, Yu, Miao, Zhang, Liping, Cardoso, Jorge, Bakas, Spyridon, Braren, Rickmer, Heinemann, Volker, Pal, Christopher, Tang, An, Kadoury, Samuel, Soler, Luc, van Ginneken, Bram, Greenspan, Hayit, Joskowicz, Leo, Menze, Bjoern

Publikováno v: Medical Image Analysis (2022) Pg. 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: http://arxiv.org/abs/1901.04056

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

Autor: Frid-Adar, Maayan, Diamant, Idit, Klang, Eyal, Amitai, Michal, Goldberger, Jacob, Greenspan, Hayit

Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the m

Externí odkaz: http://arxiv.org/abs/1803.01229

Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

Autor: Frid-Adar, Maayan, Klang, Eyal, Amitai, Michal, Goldberger, Jacob, Greenspan, Hayit

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then f

Externí odkaz: http://arxiv.org/abs/1801.02385

Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results

Autor: Ben-Cohen, Avi, Klang, Eyal, Raskin, Stephen P., Amitai, Michal Marianne, Greenspan, Hayit

In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of

Externí odkaz: http://arxiv.org/abs/1707.09585

Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.

Autor: Brem, Ofir, Elisha, David, Konen, Eli, Amitai, Michal, Klang, Eyal

Publikováno v: Abdominal Radiology; Sep2024, Vol. 49 Issue 9, p3183-3189, 7p