no code implementations • 5 Apr 2024 • Zitao Shuai, Liyue Shen
Vision-language pre-training (VLP) has arised as an efficient scheme for multimodal representation learning, but it requires large-scale multimodal data for pre-training, making it an obstacle especially for biomedical applications.
no code implementations • 8 Mar 2024 • Chenhui Zhao, Liyue Shen
Precision medicine, such as patient-adaptive treatments utilizing medical images, poses new challenges for image segmentation algorithms due to (1) the large variability across different patients and (2) the limited availability of annotated data for each patient.
no code implementations • 13 Nov 2023 • Zirui Gong, Liyue Shen, Yanjun Zhang, Leo Yu Zhang, Jingwei Wang, Guangdong Bai, Yong Xiang
By equipping AGRAMPLIFIER with the existing Byzantine-robust mechanisms, we successfully enhance the model's robustness, maintaining its fidelity and improving overall efficiency.
no code implementations • 8 Oct 2023 • Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu
In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.
1 code implementation • 16 Jul 2023 • Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen
However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.
no code implementations • 12 May 2023 • Zongyu Li, Jason Hu, Xiaojian Xu, Liyue Shen, Jeffrey A. Fessler
Phase retrieval (PR) is a crucial problem in many imaging applications.
1 code implementation • 17 May 2022 • Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
no code implementations • 30 Nov 2021 • Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A. McDermott
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
1 code implementation • NeurIPS Workshop Deep_Invers 2021 • Yang song, Liyue Shen, Lei Xing, Stefano Ermon
These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
1 code implementation • NeurIPS Workshop Deep_Invers 2021 • Liyue Shen, John Pauly, Lei Xing
The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject.
no code implementations • 25 May 2021 • Liyue Shen, Wei Zhao, Dante Capaldi, John Pauly, Lei Xing
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws.
2 code implementations • ICCV 2021 • Shih-Cheng Huang, Liyue Shen, Matthew P. Lungren, Serena Yeung
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
no code implementations • 10 Jul 2020 • Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie Anne Harmon, Thomas Hogue Sanford, Sherif Mehralivand, Peter Choyke, Bradford Wood, Daguang Xu
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
no code implementations • 25 Sep 2019 • Masoud Badiei Khuzani, Liyue Shen, Shahin Shahrampour, Lei Xing
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve finite dimensional optimization problems.
no code implementations • 25 Sep 2019 • Masoud Badiei Khuzani, Liyue Shen, Shahin Shahrampour, Lei Xing
We subsequently leverage a particle stochastic gradient descent (SGD) method to solve the derived finite dimensional optimization problem.
no code implementations • CVPR 2017 • Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei
Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.
no code implementations • ICCV 2015 • Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian
As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
Ranked #90 on Person Re-Identification on DukeMTMC-reID
no code implementations • 7 Feb 2015 • Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jiahao Bu, Qi Tian
In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem.