Search Results for author: Liyue Shen

Found 18 papers, 5 papers with code

Mitigating Heterogeneity in Federated Multimodal Learning with Biomedical Vision-Language Pre-training

no code implementations5 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.

Federated Learning Representation Learning

Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation

no code implementations8 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.

Image Segmentation Retrieval +2

AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification

no code implementations13 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.

Federated Learning

The Emergence of Reproducibility and Consistency in Diffusion Models

no code implementations8 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.

Image Generation Memorization

Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency

1 code implementation16 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.


Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

1 code implementation17 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.

Federated Learning Privacy Preserving +2

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

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.

Computed Tomography (CT)

NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction

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.

Image Reconstruction Representation Learning

A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

no code implementations25 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.

Image Reconstruction

Multi-Domain Image Completion for Random Missing Input Data

no code implementations10 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).

Brain Tumor Segmentation Disentanglement +3

A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models

no code implementations25 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.

Two-sample testing

A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adverserial Networks

no code implementations25 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.

Learning to Learn from Noisy Web Videos

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.

Action Recognition Q-Learning +1

Scalable Person Re-Identification: A Benchmark

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.

Image Retrieval Person Re-Identification

Person Re-identification Meets Image Search

no code implementations7 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.

Image Retrieval Person Re-Identification

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