Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Fidon, Lucas"'
Autor:
Saada, Thiziri Nait, Di Proietto, Valentina, Schmauch, Benoit, Von Loga, Katharina, Fidon, Lucas
Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of the same image to be independent, leading to a loss of spatial contex
Externí odkaz:
http://arxiv.org/abs/2408.00427
Autor:
Fidon, Lucas
Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their deployment into clinics. One major limitation of deep learning-based seg
Externí odkaz:
http://arxiv.org/abs/2305.17456
Autor:
Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, Maier-Hein, Lena
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in
Externí odkaz:
http://arxiv.org/abs/2212.08568
Autor:
Kofler, Florian, Ezhov, Ivan, Fidon, Lucas, Horvath, Izabela, de la Rosa, Ezequiel, LaMaster, John, Li, Hongwei, Finck, Tom, Shit, Suprosanna, Paetzold, Johannes, Bakas, Spyridon, Piraud, Marie, Kirschke, Jan, Vercauteren, Tom, Zimmer, Claus, Wiestler, Benedikt, Menze, Bjoern
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation problem, specifically glio
Externí odkaz:
http://arxiv.org/abs/2205.10355
Autor:
Kofler, Florian, Shit, Suprosanna, Ezhov, Ivan, Fidon, Lucas, Horvath, Izabela, Al-Maskari, Rami, Li, Hongwei, Bhatia, Harsharan, Loehr, Timo, Piraud, Marie, Erturk, Ali, Kirschke, Jan, Peeken, Jan C., Vercauteren, Tom, Zimmer, Claus, Wiestler, Benedikt, Menze, Bjoern
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can t
Externí odkaz:
http://arxiv.org/abs/2205.08209
Autor:
Payette, Kelly, Li, Hongwei, de Dumast, Priscille, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R., Oliveira, Bruno, Vilaça, João L., Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas, Aertsen, Michael, Vercauteren, Tom, Sobotka, Daniel, Langs, Georg, Alenyà, Mireia, Villanueva, Maria Inmaculada, Camara, Oscar, Fadida, Bella Specktor, Joskowicz, Leo, Weibin, Liao, Yi, Lv, Xuesong, Li, Mazher, Moona, Qayyum, Abdul, Puig, Domenec, Kebiri, Hamza, Zhang, Zelin, Xu, Xinyi, Wu, Dan, Liao, KuanLun, Wu, YiXuan, Chen, JinTai, Xu, Yunzhi, Zhao, Li, Vasung, Lana, Menze, Bjoern, Cuadra, Meritxell Bach, Jakab, Andras
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in th
Externí odkaz:
http://arxiv.org/abs/2204.09573
Autor:
Fidon, Lucas, Aertsen, Michael, Kofler, Florian, Bink, Andrea, David, Anna L., Deprest, Thomas, Emam, Doaa, Guffens, Frédéric, Jakab, András, Kasprian, Gregor, Kienast, Patric, Melbourne, Andrew, Menze, Bjoern, Mufti, Nada, Pogledic, Ivana, Prayer, Daniela, Stuempflen, Marlene, Van Elslander, Esther, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermin
Externí odkaz:
http://arxiv.org/abs/2204.02779
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lu
Externí odkaz:
http://arxiv.org/abs/2201.04584
Autor:
Fidon, Lucas, Shit, Suprosanna, Ezhov, Ivan, Paetzold, Johannes C., Ourselin, Sébastien, Vercauteren, Tom
Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizability to a variety of scanners and imaging protocols. In this paper, we e
Externí odkaz:
http://arxiv.org/abs/2112.13054
Autor:
Fidon, Lucas, Aertsen, Michael, Shit, Suprosanna, Demaerel, Philippe, Ourselin, Sébastien, Deprest, Jan, Vercauteren, Tom
This paper describes our method for our participation in the FeTA challenge2021 (team name: TRABIT). The performance of convolutional neural networks for medical image segmentation is thought to correlate positively with the number of training data.
Externí odkaz:
http://arxiv.org/abs/2111.02408