Search Results for author: Yingda Xia

Found 23 papers, 6 papers with code

Joint Shape Representation and Classification for Detecting PDAC

no code implementations27 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.

Classification General Classification +1

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

no code implementations9 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.

General Classification Segmentation +1

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

no code implementations29 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.

Image Segmentation Medical Image Segmentation +3

Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

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.

General Classification Image Classification +4

An Alarm System For Segmentation Algorithm Based On Shape Model

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.

Segmentation

Thickened 2D Networks for Efficient 3D Medical Image Segmentation

no code implementations2 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.

Image Segmentation Medical Image Segmentation +2

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

no code implementations15 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.

Organ Segmentation Pancreas Segmentation +1

Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

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 #8 on Anomaly Detection on Road Anomaly (using extra training data)

Anomaly Detection Autonomous Driving +3

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

no code implementations28 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.

Image Segmentation Organ Segmentation +6

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

no code implementations29 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.

Image Segmentation Pancreas Segmentation +2

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation

no code implementations20 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.

Federated Learning Image Segmentation +3

Glance-and-Gaze Vision Transformer

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.

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

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.

Federated Learning

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

no code implementations2 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.

Classification Decision Making +2

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

no code implementations10 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.

Specificity

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

no code implementations1 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.