Search Results for author: Xindi Hu

Found 11 papers, 1 papers with code

Segment Anything Model for Medical Images?

1 code implementation28 Apr 2023 Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, Sijing Liu, Haozhe Chi, Xindi Hu, Kejuan Yue, Lei LI, Vicente Grau, Deng-Ping Fan, Fajin Dong, Dong Ni

To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks.

Image Segmentation Medical Image Segmentation +3

Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip

no code implementations10 Jun 2021 Xindi Hu, LiMin Wang, Xin Yang, Xu Zhou, Wufeng Xue, Yan Cao, Shengfeng Liu, Yuhao Huang, Shuangping Guo, Ning Shang, Dong Ni, Ning Gu

In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.

Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis

no code implementations14 Apr 2022 Jiamin Liang, Xin Yang, Yuhao Huang, Haoming Li, Shuangchi He, Xindi Hu, Zejian Chen, Wufeng Xue, Jun Cheng, Dong Ni

Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.

Generative Adversarial Network Image Generation

Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling

no code implementations1 Jul 2022 Jiamin Liang, Xin Yang, Yuhao Huang, Kai Liu, Xinrui Zhou, Xindi Hu, Zehui Lin, Huanjia Luo, Yuanji Zhang, Yi Xiong, Dong Ni

First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection.

Keypoint Detection Vocal Bursts Intensity Prediction

Fine-grained Correlation Loss for Regression

no code implementations1 Jul 2022 Chaoyu Chen, Xin Yang, Ruobing Huang, Xindi Hu, Yankai Huang, Xiduo Lu, Xinrui Zhou, Mingyuan Luo, Yinyu Ye, Xue Shuang, Juzheng Miao, Yi Xiong, Dong Ni

In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses.

Attribute Image Quality Assessment +3

Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification

no code implementations5 Jun 2023 Yuhao Huang, Xin Yang, Xiaoqiong Huang, Xinrui Zhou, Haozhe Chi, Haoran Dou, Xindi Hu, Jian Wang, Xuedong Deng, Dong Ni

Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output.

Robust classification Test-time Adaptation

Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction

no code implementations28 Jun 2023 Mingyuan Luo, Xin Yang, Zhongnuo Yan, Junyu Li, Yuanji Zhang, Jiongquan Chen, Xindi Hu, Jikuan Qian, Jun Cheng, Dong Ni

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities.

FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation

no code implementations26 Aug 2023 Chaoyu Chen, Xin Yang, Rusi Chen, Junxuan Yu, Liwei Du, Jian Wang, Xindi Hu, Yan Cao, Yingying Liu, Dong Ni

In this paper, we introduce a novel Fourier-anchor-based DTS framework called Fourier Feature Pyramid Network (FFPN) to address the aforementioned issues.

Image Segmentation Semantic Segmentation

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

no code implementations30 Oct 2023 Chaoyu Chen, Xin Yang, Yuhao Huang, Wenlong Shi, Yan Cao, Mingyuan Luo, Xindi Hu, Lei Zhue, Lequan Yu, Kejuan Yue, Yuanji Zhang, Yi Xiong, Dong Ni, Weijun Huang

However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses.

Pose Estimation Self-Supervised Learning

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