1 code implementation • 29 Dec 2023 • Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Johannes C. Paetzold, Rami Al-Maskari, Luciano Höher, Hongwei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, Suprosanna Shit, Houjing Huang, Chinmay Prabhakar, Ezequiel de la Rosa, Diana Waldmannstetter, Florian Kofler, Fernando Navarro, Martin Menten, Ivan Ezhov, Daniel Rueckert, Iris Vos, Ynte Ruigrok, Birgitta Velthuis, Hugo Kuijf, Julien Hämmerli, Catherine Wurster, Philippe Bijlenga, Laura Westphal, Jeroen Bisschop, Elisa Colombo, Hakim Baazaoui, Andrew Makmur, James Hallinan, Bene Wiestler, Jan S. Kirschke, Roland Wiest, Emmanuel Montagnon, Laurent Letourneau-Guillon, Adrian Galdran, Francesco Galati, Daniele Falcetta, Maria A. Zuluaga, Chaolong Lin, Haoran Zhao, Zehan Zhang, Sinyoung Ra, Jongyun Hwang, HyunJin Park, Junqiang Chen, Marek Wodzinski, Henning Müller, Pengcheng Shi, Wei Liu, Ting Ma, Cansu Yalçin, Rachika E. Hamadache, Joaquim Salvi, Xavier Llado, Uma Maria Lal-Trehan Estrada, Valeriia Abramova, Luca Giancardo, Arnau Oliver, Jialu Liu, Haibin Huang, Yue Cui, Zehang Lin, Yusheng Liu, Shunzhi Zhu, Tatsat R. Patel, Vincent M. Tutino, Maysam Orouskhani, Huayu Wang, Mahmud Mossa-Basha, Chengcheng Zhu, Maximilian R. Rokuss, Yannick Kirchhoff, Nico Disch, Julius Holzschuh, Fabian Isensee, Klaus Maier-Hein, Yuki Sato, Sven Hirsch, Susanne Wegener, Bjoern Menze
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
no code implementations • 27 Oct 2023 • Jiesi Hu, Yanwu Yang, Xutao Guo, Jinghua Wang, Ting Ma
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data.
1 code implementation • 25 May 2023 • Pengcheng Shi, Xutao Guo, Yanwu Yang, Chenfei Ye, Ting Ma
Motivated by the successful breakthroughs of graph neural networks (GNN) in capturing topological properties and non-Euclidean relationships across various fields, we propose NexToU, a novel hybrid architecture for medical image segmentation.
1 code implementation • 28 Feb 2023 • Jiesi Hu, Yanwu Yang, Xutao Guo, Ting Ma
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health.
no code implementations • 27 Oct 2022 • Xutao Guo, Yanwu Yang, Chenfei Ye, Shang Lu, Yang Xiang, Ting Ma
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance.
no code implementations • 25 Oct 2022 • Yanwu Yang, Xutao Guo, Zhikai Chang, Chenfei Ye, Yang Xiang, Ting Ma
Graph neural networks have been proven to be of great importance in modeling brain connectome networks and relating disease-specific patterns.
no code implementations • 19 Sep 2022 • Yanwu Yang, Xutao Guo, Zhikai Chang, Chenfei Ye, Yang Xiang, Haiyan Lv, Ting Ma
The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease.
no code implementations • 15 Sep 2021 • Yanwu Yang, Xutao Guo, Yiwei Pan, Pengcheng Shi, Haiyan Lv, Ting Ma
We exploit the medical image segmentation uncertainty quantification by measuring segmentation performance with multiple annotations in a supervised learning manner and propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders.
1 code implementation • 1 Mar 2021 • Yang Yang, Jiancong Chen, Ruixuan Wang, Ting Ma, Lingwei Wang, Jie Chen, Wei-Shi Zheng, Tong Zhang
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images.
no code implementations • 18 Sep 2020 • Chen-Xu Hao, Ting Ma
In this paper we mainly discuss sharp lower and upper bounds for the length of longest consecutive switches in IID Bernoulli sequences.
Probability 60F15