Search Results for author: Zilin Wang

Found 11 papers, 5 papers with code

A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy

no code implementations9 Dec 2024 Zilin Wang, Shengqi Chen, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Guohua Wu, Yuan Tang, Jiayun Chen

Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision. Methods:We analyzed 106, 000 MR images from 10 patients with liver metastasis, captured with the Elekta Unity MR-LINAC. Our No-Reference Quality Assessment Model includes:1)image preprocessing to enhance visibility of key diagnostic features;2)feature extraction and directional analysis using MSCN coefficients across four directions to capture textural attributes and gradients, vital for identifying image features and potential distortions;3)integrative Quality Index(QI)calculation, which integrates features via AGGD parameter estimation and K-means clustering. The QI, based on a weighted MAD computation of directional scores, provides a comprehensive image quality measure, robust against outliers. LOO-CV assessed model generalizability and performance. Tumor tracking algorithm performance was compared with and without preprocessing to verify tracking accuracy enhancements. Results:Preprocessing significantly improved image quality, with the QI showing substantial positive changes and surpassing other metrics. After normalization, the QI's average value was 79. 6 times higher than CNR, indicating improved image definition and contrast. It also showed higher sensitivity in detail recognition with average values 6. 5 times and 1. 7 times higher than Tenengrad gradient and entropy. The tumor tracking algorithm confirmed significant tracking accuracy improvements with preprocessed images, validating preprocessing effectiveness. Conclusions:This study introduces a novel no-reference image quality evaluation method based on automated distortion recognition, offering a new quality control tool for MRIgRT tumor tracking. It enhances clinical application accuracy and facilitates medical image quality assessment standardization, with significant clinical and research value.

No-Reference Image Quality Assessment Unity

A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation

no code implementations12 Nov 2024 Shengqi Chen, Zilin Wang, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Jiayun Chen, Guohua Wu, Yuan Tang

This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation.

No-Reference Image Quality Assessment

Improving Generalization and Convergence by Enhancing Implicit Regularization

1 code implementation31 May 2024 Mingze Wang, Jinbo Wang, Haotian He, Zilin Wang, Guanhua Huang, Feiyu Xiong, Zhiyu Li, Weinan E, Lei Wu

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence.

Image Classification

Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations

no code implementations18 Dec 2023 Zilin Wang, Haolin Zhuang, Lu Li, Yinmin Zhang, Junjie Zhong, Jun Chen, Yu Yang, Boshi Tang, Zhiyong Wu

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models.

Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations

no code implementations13 Oct 2023 Lu Li, Yuxin Pan, RuoBing Chen, Jie Liu, Zilin Wang, Yu Liu, Zhiheng Li

Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations.

Contrastive Learning

Normalization Enhances Generalization in Visual Reinforcement Learning

1 code implementation1 Jun 2023 Lu Li, Jiafei Lyu, Guozheng Ma, Zilin Wang, Zhenjie Yang, Xiu Li, Zhiheng Li

Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce.

reinforcement-learning Reinforcement Learning +1

FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement

2 code implementations23 Mar 2022 Jun Chen, Zilin Wang, Deyi Tuo, Zhiyong Wu, Shiyin Kang, Helen Meng

Previously proposed FullSubNet has achieved outstanding performance in Deep Noise Suppression (DNS) Challenge and attracted much attention.

Speech Enhancement

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