Search Results for author: Ting Ma

Found 10 papers, 4 papers with code

TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA

1 code implementation29 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, 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.

Anatomy Benchmarking +1

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

no code implementations27 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.

Denoising Image Segmentation +3

NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation

1 code implementation25 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.

Image Segmentation Medical Image Segmentation +1

Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation

no code implementations27 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.

Conditional Image Generation Denoising +4

Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification

no code implementations25 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.

Estimating Brain Age with Global and Local Dependencies

no code implementations19 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.

Feature Engineering Inductive Bias

Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net

no code implementations15 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.

Image Segmentation Medical Image Segmentation +3

Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning

1 code implementation1 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.

Generative Adversarial Network Weakly-supervised Learning

Limiting behaviors for longest consecutive switches in an IID Bernoulli sequence

no code implementations18 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

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