no code implementations • 1 Aug 2023 • Hexin Dong, Jiawen Yao, Yuxing Tang, Mingze Yuan, Yingda Xia, Jian Zhou, Hong Lu, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Yu Shi, Ling Zhang
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.
no code implementations • 17 Jul 2023 • Ke Yan, Xiaoli Yin, Yingda Xia, Fakai Wang, Shu Wang, Yuan Gao, Jiawen Yao, Chunli Li, Xiaoyu Bai, Jingren Zhou, Ling Zhang, Le Lu, Yu Shi
Liver tumor segmentation and classification are important tasks in computer aided diagnosis.
no code implementations • 10 Jul 2023 • Mingze Yuan, Yingda Xia, Xin Chen, Jiawen Yao, Junli Wang, Mingyan Qiu, Hexin Dong, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Ling Zhang
In our experiments, the proposed method achieves a sensitivity of 85. 0% and specificity of 92. 6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal.
no code implementations • CVPR 2023 • Mingze Yuan, Yingda Xia, Hexin Dong, ZiFan Chen, Jiawen Yao, Mingyan Qiu, Ke Yan, Xiaoli Yin, Yu Shi, Xin Chen, Zaiyi Liu, Bin Dong, Jingren Zhou, Le Lu, Ling Zhang, Li Zhang
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant.
no code implementations • 2 Mar 2023 • Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang
Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.
no code implementations • 28 Jan 2023 • Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan.
1 code implementation • CVPR 2022 • Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
1 code implementation • 10 Jun 2021 • Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
1 code implementation • NeurIPS 2021 • Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan Yuille, Wei Shen
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
no code implementations • 20 Apr 2021 • Yingda Xia, Dong Yang, Wenqi Li, Andriy Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, Peng An, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Anna Ierardi, Alan Yuille, Holger Roth
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
no code implementations • 29 Oct 2020 • Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.
no code implementations • 28 Jun 2020 • Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth
Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.
no code implementations • 18 Mar 2020 • Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
1 code implementation • ECCV 2020 • Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, Alan Yuille
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis.
Ranked #7 on
Anomaly Detection
on Road Anomaly
(using extra training data)
no code implementations • 15 Oct 2019 • Jinzheng Cai, Yingda Xia, Dong Yang, Daguang Xu, Lin Yang, Holger Roth
However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs.
no code implementations • 2 Apr 2019 • Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information.
no code implementations • ICLR 2019 • Fengze Liu, Yingda Xia, Dong Yang, Alan Yuille, Daguang Xu
Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data. The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks.
1 code implementation • CVPR 2019 • Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, Alan L. Yuille
We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.
no code implementations • 29 Nov 2018 • Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth
Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.
no code implementations • 9 Jul 2018 • Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans.
no code implementations • 27 Apr 2018 • Fengze Liu, Lingxi Xie, Yingda Xia, Elliot K. Fishman, Alan L. Yuille
Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting.
no code implementations • 2 Apr 2018 • Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman, Alan L. Yuille
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.
no code implementations • 1 Dec 2017 • Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images.