Search

Your search keyword '"Zhang, Ruru"' showing total 11 results
Start Over Author "Zhang, Ruru" Search Limiters Full Text
11 results on '"Zhang, Ruru"'

2. KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD

Author

E, Haihong, He, Jiawen, Hu, Tianyi, Wang, Lifei, Yuan, Lifei, Zhang, Ruru, and Song, Meina

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal Vessels (PCV). However, due to the difficulty in data collection and the similarity between images, most studies have only achieved the coarse-grained classification of wet-AMD rather than a finer-grained one of wet-AMD subtypes. To solve this issue, in this paper we propose a Knowledge-driven Fine-grained Wet-AMD Classification Model (KFWC), to classify fine-grained diseases with insufficient data. With the introduction of a priori knowledge of 10 lesion signs of input images into the KFWC, we aim to accelerate the KFWC by means of multi-label classification pre-training, to locate the decisive image features in the fine-grained disease classification task and therefore achieve better classification. Simultaneously, the KFWC can also provide good interpretability and effectively alleviate the pressure of data collection and annotation in the field of fine-grained disease classification for wet-AMD. The experiments demonstrate the effectiveness of the KFWC which reaches 99.71% in AU-ROC scores, and its considerable improvements over the data-driven w/o Knowledge and ophthalmologists, with the rates of 6.69% over the strongest baseline and 4.14% over ophthalmologists.

Published
2021

4. Precise diagnosis of cardiac‐cerebral vascular diseases with magnetic resonance imaging‐based nanoprobes

Author

Li, Wenyue, Zhang, Ruru, Zhang, Xinyi, Wu, Shuai, Ma, Tiancong, Hou, Yi, Zeng, Jianfeng, and Gao, Mingyuan

Abstract

Cardiac‐cerebral vascular diseases (CCVDs) are acknowledged as a major threat to public health, leading to more than one‐third of all deaths worldwide. The complex anatomical structure and immune features of blood vessels significantly affect the development of CCVDs, and magnetic resonance angiography (MRA) is one of the main diagnostic approaches for the accurate diagnosis and prognosis of CCVDs. However, MRA suffers from intrinsic problems derived from its blood flow‐dependency, and the clinical Gd‐chelating contrast agents are limited by their rapid vascular extravasation. Over the past decade, spurred on by nanoscience and nanotechnology, numerous contrast agents based on magnetic nanomaterials have been developed to enhance the contrast of MRA, with these including iron oxide nanoparticles, rare earth‐doped nanoparticles, and metal‐organic coordination polymers. The molecular MR imaging of vasculopathy using specific nanoprobes has been explored to obtain a better understanding of the molecular aspects of CCVDs. In this review, the state of the art in MRA nanoprobes is introduced, and recent achievements in the diagnosis of CCVDs using MR imaging are summarized. Additionally, the future prospects and limitations of MRA based on nanoprobes are discussed. The current review provides methodological designs and ideas for subsequent MRA nanoprobes. Based on numerous novel nanoprobes, the molecular MR imaging of vasculopathy has been explored for the accurate diagnosis and prognosis of cardiac‐cerebral vascular diseases (CCVDs), including acute ischemic stroke, coronary heart disease, thrombus, etc.

Published
2024
Full Text
View/download PDF

5. Exploring atherosclerosis imaging with contrast-enhanced MRI using PEGylated ultrasmall iron oxide nanoparticles

Author

Zhang, Ruru, primary, Lu, Kuan, additional, Xiao, Li, additional, Hu, Xuelan, additional, Cai, Wu, additional, Liu, Linjiang, additional, Liu, Yan, additional, Li, Weihua, additional, Zhou, Hui, additional, Qian, Zhiyuan, additional, Wang, Sixia, additional, Chen, Can, additional, Zeng, Jianfeng, additional, and Gao, Mingyuan, additional

Published
2023
Full Text
View/download PDF

9. Localizing Failure Root Causes in a Microservice through Causality Inference

Author

Meng, Yuan, Zhang, Shenglin, Sun, Yongqian, Zhang, Ruru, Hu, Zhilong, Zhang, Yiyin, Jia, Chenyang, Wang, Zhaogang, Pei, Dan, Meng, Yuan, Zhang, Shenglin, Sun, Yongqian, Zhang, Ruru, Hu, Zhilong, Zhang, Yiyin, Jia, Chenyang, Wang, Zhaogang, and Pei, Dan

Abstract

An increasing number of Internet applications are applying microservice architecture due to its flexibility and clear logic. The stability of microservice is thus vitally important for these applications' quality of service. Accurate failure root cause localization can help operators quickly recover microservice failures and mitigate loss. Although cross-microservice failure root cause localization has been well studied, how to localize failure root causes in a microservice so as to quickly mitigate this microservice has not yet been studied. In this work, we propose a framework, MicroCause, to accurately localize the root cause monitoring indicators in a microservice. MicroCause combines a simple yet effective path condition time series (PCTS) algorithm which accurately captures the sequential relationship of time series data, and a novel temporal cause oriented random walk (TCORW) method integrating the causal relationship, temporal order, and priority information of monitoring data. We evaluate MicroCause based on 86 real-world failure tickets collected from a top tier global online shopping service. Our experiments show that the top 5 accuracy (AC@5) of MicroCause for intra-microservice failure root cause localization is 98.7%, which is greatly higher (by 33.4 %) than the best baseline method.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results