Search

Your search keyword '"Cao, Chentao"' showing total 5 results
Start Over Author "Cao, Chentao" Publication Year Range This year
5 results on '"Cao, Chentao"'

1. Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection

Author

Cao, Chentao, Zhong, Zhun, Zhou, Zhanke, Liu, Yang, Liu, Tongliang, and Han, Bo

Subjects
Computer Science - Machine Learning
Abstract

Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distribution (ID) data, has been possible with the advent of vision-language models like CLIP. Existing methods build a text-based classifier with only closed-set labels. However, this largely restricts the inherent capability of CLIP to recognize samples from large and open label space. In this paper, we propose to tackle this constraint by leveraging the expert knowledge and reasoning capability of large language models (LLM) to Envision potential Outlier Exposure, termed EOE, without access to any actual OOD data. Owing to better adaptation to open-world scenarios, EOE can be generalized to different tasks, including far, near, and fine-grained OOD detection. Technically, we design (1) LLM prompts based on visual similarity to generate potential outlier class labels specialized for OOD detection, as well as (2) a new score function based on potential outlier penalty to distinguish hard OOD samples effectively. Empirically, EOE achieves state-of-the-art performance across different OOD tasks and can be effectively scaled to the ImageNet-1K dataset. The code is publicly available at: https://github.com/tmlr-group/EOE., Comment: ICML 2024

Published
2024

2. Synthesizing PET images from high‐field and ultra‐high‐field MR images using joint diffusion attention model.

Author

Xie, Taofeng, Cao, Chentao, Cui, Zhuo‐xu, Guo, Yu, Wu, Caiying, Wang, Xuemei, Li, Qingneng, Hu, Zhanli, Sun, Tao, Sang, Ziru, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Jin, Qiyu, Zeng, Hongwu, Chen, Guoqing, and Wang, Haifeng

Subjects
*POSITRON emission tomography, *MAGNETIC resonance imaging, *DISTRIBUTION (Probability theory), *ALZHEIMER'S disease, *GAUSSIAN distribution
Abstract

Background: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high‐field and ultra‐high‐field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high‐resolution MRI improve the quality of PET images? Purpose: This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high‐field and ultra‐high‐field MRI. Methods: From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor‐corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevin dynamics. Results: Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state‐of‐the‐art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra‐high‐field MRI exhibit marked improvements in signal‐to‐noise characteristics when contrasted with those generated from high‐field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer's disease. Conclusions: This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTA models. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

3. Annihilation‐Net: Learned annihilation relation for dynamic MR imaging.

Author

Cao, Chentao, Cui, Zhuo‐Xu, Zhu, Qingyong, Liu, Congcong, Liang, Dong, and Zhu, Yanjie

Subjects
*MAGNETIC resonance imaging, *CONTRAST-enhanced magnetic resonance imaging, *COMPRESSED sensing, *DEEP learning, *MATRIX multiplications, *REGULARIZATION parameter, *MAGNETIC resonance
Abstract

Background: Deep learning methods driven by the low‐rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability. Purpose: This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation‐Net and use it for accelerating dynamic MRI. Methods: Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low‐rankness. We employ low‐rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi‐quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation‐Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation‐Net. Results: Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively. Conclusions: The proposed Annihilation‐Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

4. High-Frequency Space Diffusion Model for Accelerated MRI

Author

Cao, Chentao, Cui, Zhuo-Xu, Wang, Yue, Liu, Shaonan, Chen, Taijin, Zheng, Hairong, Liang, Dong, and Zhu, Yanjie

Abstract

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of ${k}$ -space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or ${k}$ -space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.

Published
2024
Full Text
View/download PDF

5. A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Author

Wang, Wenxin, Cui, Zhuo-Xu, Cheng, Guanxun, Cao, Chentao, Xu, Xi, Liu, Ziwei, Wang, Haifeng, Qi, Yulong, Liang, Dong, and Zhu, Yanjie

Abstract

Accuratedetection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results