SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

Autor: Chatterjee, Soumick, Mattern, Hendrik, Dörner, Marc, Sciarra, Alessandro, Dubost, Florian, Schnurre, Hannes, Khatun, Rupali, Yu, Chun-Chih, Hsieh, Tsung-Lin, Tsai, Yi-Shan, Fang, Yi-Zeng, Yang, Yung-Ching, Huang, Juinn-Dar, Xu, Marshall, Liu, Siyu, Ribeiro, Fernanda L., Bollmann, Saskia, Chintalapati, Karthikesh Varma, Radhakrishna, Chethan Mysuru, Kumara, Sri Chandana Hudukula Ram, Sutrave, Raviteja, Qayyum, Abdul, Mazher, Moona, Razzak, Imran, Rodero, Cristobal, Niederren, Steven, Lin, Fengming, Xia, Yan, Wang, Jiacheng, Qiu, Riyu, Wang, Liansheng, Panah, Arya Yazdan, Jurdi, Rosana El, Fu, Guanghui, Arslan, Janan, Vaillant, Ghislain, Valabregue, Romain, Dormont, Didier, Stankoff, Bruno, Colliot, Olivier, Vargas, Luisa, Chacón, Isai Daniel, Pitsiorlas, Ioannis, Arbeláez, Pablo, Zuluaga, Maria A., Schreiber, Stefanie, Speck, Oliver, Nürnberger, Andreas

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe condi

Externí odkaz: http://arxiv.org/abs/2411.09593

PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations

Autor: Zheng, Yang, Zhao, Qingqing, Yang, Guandao, Yifan, Wang, Xiang, Donglai, Dubost, Florian, Lagun, Dmitry, Beeler, Thabo, Tombari, Federico, Guibas, Leonidas, Wetzstein, Gordon

Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework

Externí odkaz: http://arxiv.org/abs/2404.04421

Exploring adversarial attacks in federated learning for medical imaging

Autor: Darzi, Erfan, Dubost, Florian, Sijtsema, N. M., van Ooijen, P. M. A

Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical image analysis against such a

Externí odkaz: http://arxiv.org/abs/2310.06227

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Autor: Li, Jianning, Zhou, Zongwei, Yang, Jiancheng, Pepe, Antonio, Gsaxner, Christina, Luijten, Gijs, Qu, Chongyu, Zhang, Tiezheng, Chen, Xiaoxi, Li, Wenxuan, Wodzinski, Marek, Friedrich, Paul, Xie, Kangxian, Jin, Yuan, Ambigapathy, Narmada, Nasca, Enrico, Solak, Naida, Melito, Gian Marco, Vu, Viet Duc, Memon, Afaque R., Schlachta, Christopher, De Ribaupierre, Sandrine, Patel, Rajnikant, Eagleson, Roy, Chen, Xiaojun, Mächler, Heinrich, Kirschke, Jan Stefan, de la Rosa, Ezequiel, Christ, Patrick Ferdinand, Li, Hongwei Bran, Ellis, David G., Aizenberg, Michele R., Gatidis, Sergios, Küstner, Thomas, Shusharina, Nadya, Heller, Nicholas, Andrearczyk, Vincent, Depeursinge, Adrien, Hatt, Mathieu, Sekuboyina, Anjany, Löffler, Maximilian, Liebl, Hans, Dorent, Reuben, Vercauteren, Tom, Shapey, Jonathan, Kujawa, Aaron, Cornelissen, Stefan, Langenhuizen, Patrick, Ben-Hamadou, Achraf, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Bolelli, Federico, Grana, Costantino, Lumetti, Luca, Salehi, Hamidreza, Ma, Jun, Zhang, Yao, Gharleghi, Ramtin, Beier, Susann, Sowmya, Arcot, Garza-Villarreal, Eduardo A., Balducci, Thania, Angeles-Valdez, Diego, Souza, Roberto, Rittner, Leticia, Frayne, Richard, Ji, Yuanfeng, Ferrari, Vincenzo, Chatterjee, Soumick, Dubost, Florian, Schreiber, Stefanie, Mattern, Hendrik, Speck, Oliver, Haehn, Daniel, John, Christoph, Nürnberger, Andreas, Pedrosa, João, Ferreira, Carlos, Aresta, Guilherme, Cunha, António, Campilho, Aurélio, Suter, Yannick, Garcia, Jose, Lalande, Alain, Vandenbossche, Vicky, Van Oevelen, Aline, Duquesne, Kate, Mekhzoum, Hamza, Vandemeulebroucke, Jef, Audenaert, Emmanuel, Krebs, Claudia, van Leeuwen, Timo, Vereecke, Evie, Heidemeyer, Hauke, Röhrig, Rainer, Hölzle, Frank, Badeli, Vahid, Krieger, Kathrin, Gunzer, Matthias, Chen, Jianxu, van Meegdenburg, Timo, Dada, Amin, Balzer, Miriam, Fragemann, Jana, Jonske, Frederic, Rempe, Moritz, Malorodov, Stanislav, Bahnsen, Fin H., Seibold, Constantin, Jaus, Alexander, Marinov, Zdravko, Jaeger, Paul F., Stiefelhagen, Rainer, Santos, Ana Sofia, Lindo, Mariana, Ferreira, André, Alves, Victor, Kamp, Michael, Abourayya, Amr, Nensa, Felix, Hörst, Fabian, Brehmer, Alexander, Heine, Lukas, Hanusrichter, Yannik, Weßling, Martin, Dudda, Marcel, Podleska, Lars E., Fink, Matthias A., Keyl, Julius, Tserpes, Konstantinos, Kim, Moon-Sung, Elhabian, Shireen, Lamecker, Hans, Zukić, Dženan, Paniagua, Beatriz, Wachinger, Christian, Urschler, Martin, Duong, Luc, Wasserthal, Jakob, Hoyer, Peter F., Basu, Oliver, Maal, Thomas, Witjes, Max J. H., Schiele, Gregor, Chang, Ti-chiun, Ahmadi, Seyed-Ahmad, Luo, Ping, Menze, Bjoern, Reyes, Mauricio, Deserno, Thomas M., Davatzikos, Christos, Puladi, Behrus, Fua, Pascal, Yuille, Alan L., Kleesiek, Jens, Egger, Jan

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit s

Externí odkaz: http://arxiv.org/abs/2308.16139

ATCON: Attention Consistency for Vision Models

Autor: Mirzazadeh, Ali, Dubost, Florian, Pike, Maxwell, Maniar, Krish, Zuo, Max, Lee-Messer, Christopher, Rubin, Daniel

Attention--or attribution--maps methods are methods designed to highlight regions of the model's input that were discriminative for its predictions. However, different attention maps methods can highlight different regions of the input, with sometime

Externí odkaz: http://arxiv.org/abs/2210.09705