Author details
Author's Published Works
Author details
Showing 1-6 of 6 results
- Sort
Filter Results
Show
Sort By
Results
DiffMIC-v2: Medical Image Classification via Improved Diffusion Network
Yijun Yang;Huazhu Fu;Angelica I. Aviles-Rivero;Zhaohu Xing;Lei Zhu
IEEE Transactions on Medical Imaging
Year: 2025 | Early Access Article |
Recently, Denoising Diffusion Models have achieved outstanding success in generative image modeling and attracted significant attention in the computer vision community. Although a substantial amount of diffusion-based research has focused on generative tasks, few studies apply diffusion models to medical diagnosis. In this paper, we propose a diffusion-based network (named DiffMIC-v2) to address ...Show More
Cascaded Inner-Outer Clip Retformer for Ultrasound Video Object Segmentation
Jialu Li;Lei Zhu;Zhaohu Xing;Baoliang Zhao;Ying Hu;Faqin Lv;Qiong Wang
IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Early Access Article |
Computer-aided ultrasound (US) imaging is an important prerequisite for early clinical diagnosis and treatment. Due to the harsh ultrasound (US) image quality and the blurry tumor area, recent memory-based video object segmentation models (VOS) achieve frame-level segmentation by performing intensive similarity matching among the past frames which could inevitably result in computational redundanc...Show More
NTIRE 2024 Image Shadow Removal Challenge Report
Florin-Alexandru Vasluianu;Tim Seizinger;Zhuyun Zhou;Zongwei Wu;Cailian Chen;Radu Timofte;Wei Dong;Han Zhou;Yuqiong Tian;Jun Chen;Xueyang Fu;Xin Lu;Yurui Zhu;Xi Wang;Dong Li;Jie Xiao;Yunpeng Zhang;Zheng-Jun Zha;Zhao Zhang;Suiyi Zhao;Bo Wang;Yan Luo;Yanyan Wei;Zhihao Zhao;Long Sun;Tingting Yang;Jinshan Pan;Jiangxin Dong;Jinhui Tang;Bilel Benjdira;Mohammed Nassif;Anis Koubaa;Ahmed Elhayek;Anas M. Ali;Kyotaro Tokoro;Kento Kawai;Kaname Yokoyama;Takuya Seno;Yuki Kondo;Norimichi Ukita;Chenghua Li;Bo Yang;Zhiqi Wu;Gao Chen;Yihan Yu;Sixiang Chen;Kai Zhang;Tian Ye;Wenbin Zou;Yunlong Lin;Zhaohu Xing;Jinbin Bai;Wenhao Chai;Lei Zhu;Ritik Maheshwari;Rakshank Verma;Rahul Tekchandani;Praful Hambarde;Satya Narayan Tazi;Santosh Kumar Vipparthi;Subrahmanyam Murala;Jaeho Lee;Seongwan Kim;Sharif S M A;Nodirkhuja Khujaev;Roman Tsoy;Fan Gao;Weidan Yan;Wenze Shao;Dengyin Zhang;Bin Chen;Siqi Zhang;Yanxin Qian;Yuanbin Chen;Yuanbo Zhou;Tong Tong;Rongfeng Wei;Ruiqi Sun;Yue Liu;Nikhil Akalwadi;Amogh Joshi;Sampada Malagi;Chaitra Desai;Ramesh Ashok Tabib;Uma Mudenagudi;Ali Murtaza;Uswah Khairuddin;Ahmad ’Athif Mohd Faudzi;Adinath Dukre;Vivek Deshmukh;Shruti S. Phutke;Ashutosh Kulkarni;Santosh Kumar Vipparthi;Anil Gonde;Subrahmanyam Murala;Arun karthik K;Manasa N;Shri Hari Priya;Wei Hao;Xingzhuo Yan;Minghan Fu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (21)
This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Tr...Show More
NTIRE 2024 Image Shadow Removal Challenge Report
Florin-Alexandru Vasluianu;Tim Seizinger;Zhuyun Zhou;Zongwei Wu;Cailian Chen;Radu Timofte;Wei Dong;Han Zhou;Yuqiong Tian;Jun Chen;Xueyang Fu;Xin Lu;Yurui Zhu;Xi Wang;Dong Li;Jie Xiao;Yunpeng Zhang;Zheng-Jun Zha;Zhao Zhang;Suiyi Zhao;Bo Wang;Yan Luo;Yanyan Wei;Zhihao Zhao;Long Sun;Tingting Yang;Jinshan Pan;Jiangxin Dong;Jinhui Tang;Bilel Benjdira;Mohammed Nassif;Anis Koubaa;Ahmed Elhayek;Anas M. Ali;Kyotaro Tokoro;Kento Kawai;Kaname Yokoyama;Takuya Seno;Yuki Kondo;Norimichi Ukita;Chenghua Li;Bo Yang;Zhiqi Wu;Gao Chen;Yihan Yu;Sixiang Chen;Kai Zhang;Tian Ye;Wenbin Zou;Yunlong Lin;Zhaohu Xing;Jinbin Bai;Wenhao Chai;Lei Zhu;Ritik Maheshwari;Rakshank Verma;Rahul Tekchandani;Praful Hambarde;Satya Narayan Tazi;Santosh Kumar Vipparthi;Subrahmanyam Murala;Jaeho Lee;Seongwan Kim;Sharif S M A;Nodirkhuja Khujaev;Roman Tsoy;Fan Gao;Weidan Yan;Wenze Shao;Dengyin Zhang;Bin Chen;Siqi Zhang;Yanxin Qian;Yuanbin Chen;Yuanbo Zhou;Tong Tong;Rongfeng Wei;Ruiqi Sun;Yue Liu;Nikhil Akalwadi;Amogh Joshi;Sampada Malagi;Chaitra Desai;Ramesh Ashok Tabib;Uma Mudenagudi;Ali Murtaza;Uswah Khairuddin;Ahmad ’Athif Mohd Faudzi;Adinath Dukre;Vivek Deshmukh;Shruti S. Phutke;Ashutosh Kulkarni;Santosh Kumar Vipparthi;Anil Gonde;Subrahmanyam Murala;Arun karthik K;Manasa N;Shri Hari Priya;Wei Hao;Xingzhuo Yan;Minghan Fu
Year: 2024 | Conference Paper |
Learning Diffusion Texture Priors for Image Restoration
Tian Ye;Sixiang Chen;Wenhao Chai;Zhaohu Xing;Jing Qin;Ge Lin;Lei Zhu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Diffusion Models have shown remarkable performance in image generation tasks, which are capable of generating diverse and realistic image content. When adopting diffusion models for image restoration, the crucial challenge lies in how to preserve high-level image fidelity in the random-ness diffusion process and generate accurate background structures and realistic texture details. In this paper, ...Show More
Hybrid Masked Image Modeling for 3D Medical Image Segmentation
Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time. This paper presents HybridMIM, a novel hybrid self...Show More
Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation
Junting Zhao;Zhaohu Xing;Zhihao Chen;Liang Wan;Tong Han;Huazhu Fu;Lei Zhu
Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typically excel at capturing fine local features. Furthermore, there is a wealth of complementary information between their segmentation predictions. Inspi...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.