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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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FundusGAN: A Hierarchical Feature-Aware Generative Framework for high-Fidelity Fundus Image Generation

arXiv

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arXiv 2025年

作者： Hou, Qingshan Wang, Meng Cao, Peng Ke, Zou Liu, Xiaoli Fu, Huazhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking 25

Towards high-throughput and low-latency billion-scale vector...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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Principle of a one-step MSD adder for a ternary optical computer

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science China(Information sciences) 2014年第1期57卷 86-95页

作者： SHEN YunFu PAN Lei School of Computer Engineering and Science High Performance Computing Center Shanghai University

While investigating a three-step MSD adder and the reliability thereof for ternary optical computers(TOC),the principle of one-step MSD addition was *** important concept,the mid-bit transform,is ***,the important mid-bit transform table(MTT)for 3-bit MSD input and the corresponding mid-bit transform unit(MTU)are *** the principle,the concept structure of a one-step MSD adder is proposed such that the sum of the addition can be obtained easily in one step in *** restricting the input symbols,a simplified MTT is obtained and the principle of a one-step adder with restricted input symbols is introduced,together with the corresponding simplified concept structure of a one-step MSD *** examples are presented to demonstrate the principles of one-step MSD *** work lays the theoretic foundation for the design of an optical MSD adder in future study.

关键词： ternary optical computer(TOC) one-step MSD adder mid-bit transform unit(MTU) mid-bit transform table(MTT

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WeakPolyp-SAM: Segment Anything Model-driven weakly-supervised polyp segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Zhao, Yiming Zhou, Tao Gu, Yunqi Zhou, Yi Zhang, Yizhe Wu, Ye Fu, Huazhu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China School of Computer Science and Engineering Southeast University Nanjing 211189 China Institute of High Performance Computing A*STAR Singapore

Accurate polyp segmentation is essential for early detection of polyps, which is of significant clinical importance for the prevention of colorectal cancer. Although various fully-supervised deep learning techniques have been developed for polyp segmentation, pixel-wise annotation by physicians during diagnosis is both time-consuming and expensive. Meanwhile, visual foundation models such as the Segment Anything Model (SAM) have demonstrated remarkable performance in general segmentation tasks. However, directly applying SAM to medical segmentation may not yield satisfactory results due to the lack of medical knowledge. In this paper, we propose a novel SAM-driven weakly-supervised polyp segmentation framework (termed WeakPolyp-SAM), which enables a collaborative learning process between our segmentation network and SAM to boost the model performance. Specifically, we propose a Cross-aware Feature Aggregation Network (CFANet) to effectively aggregate cross-level features and leverage global cues for weakly-supervised polyp segmentation. Within CFANet, we propose a Cross-level Fusion Module (CFM) that integrates the adjacent features to enhance the representation capabilities of different resolution features. Additionally, a Local-and-global Context Decoder (LCD) is presented to capture richer features across multiple levels. Moreover, we present a box-augmentation strategy that combines the segmentation maps generated by CFANet with scribble annotations to create more precise prompts. These prompts are then fed into SAM, generating segmentation SAM-driven masks, which provide additional supervision to effectively train CFANet. Furthermore, we present an image-level filtering mechanism to filter out unreliable masks. Experimental results demonstrate that our WeakPolyp-SAM outperforms state-of-the-art weakly-supervised segmentation methods. The scribble-annotated datasets and code will be released at https://***/taozh2017/WeakPolyp-SAM. © 2025 Elsevier B.V.

关键词： Cross-level fusion Local-and-global context decoder Polyp segmentation Segment Anything Model

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The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

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Wuhan University Journal of Natural sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

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Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications

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EPMA Journal 2025年 1-15页

作者： Pushpanathan, Krithi Bai, Yang Lei, Xiaofeng Goh, Jocelyn Hui Lin Xue, Can Can Yew, Samantha Min Er Chee, Miaoli Quek, Ten Cheer Peng, Qingsheng Soh, Zhi Da Yu, Marco Chak Yan Zhou, Jun Wang, Yaxing Jonas, Jost B. Wang, Xiaofei Sim, Xueling Tai, E. Shyong Sabanayagam, Charumathi Goh, Rick Siow Mong Liu, Yong Cheng, Ching-Yu Tham, Yih-Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Singapore Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Ophthalmology & Visual Sciences Academic Clinical Programme (EYE ACP) Duke-NUS Medical School Singapore Singapore Ophthalmology and Visual Science Key Lab Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University BeijingBeijing China Rothschild Foundation Hospital Institut Français de Myopie Paris France Key Laboratory of Biomechanics and Mechanobiology Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Saw Swee Hock School of Public Health National University of Singaporeand National University Health System Singapore Singapore Duke-NUS Medical School Singapore Singapore Precision Health Research Singapore Singapore Department of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

Objective: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images. Methods: ViT-based models with ResNet-50 for patch projection were trained on images from the UK Biobank (n = 113,713) and Singapore Epidemiology of Eye Diseases study (n = 17,783), and externally validated on the Singapore Prospective Study Programme (n = 7,793) and the Beijing Eye Study (n = 6064). Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) for multiple tasks: detecting disease, identifying poorly controlled and well-controlled cases, distinguishing between poorly and well-controlled cases, and detecting pre-diabetes or pre-hypertension. Results: The models demonstrated strong performance in detecting disease presence, with AUROC values of 0.820 for diabetes and 0.781 for hypertension in internal testing. External validation showed AUROCs ranging from 0.635 to 0.755 for diabetes, and 0.727 to 0.832 for hypertension. For identifying poorly controlled cases, the performance remained high with AUROCs of 0.871 (internal) and 0.655–0.851 (external) for diabetes, and 0.853 (internal) and 0.792–0.915 (external) for hypertension. Detection of well-controlled cases also yielded promising results for diabetes (0.802 [internal];0.675–0.838 [external]), and hypertension (0.740 [internal] and 0.675–0.807 [external]). In distinguishing between poorly and well-controlled disease, AUROCs were more modest with 0.630 (internal) and 0.512–0.547 (external) for diabetes, and 0.651 (internal) and 0.639–0.683 (external) for hypertension. For pre-disease detection, the models achieved AUROCs of 0.746

关键词： Deep learning Diabetes Health risk assessment Hypertension Improved individual outcomes Innovative screening programs Opportunistic screening Predictive Preventive Personalized Medicine (PPPM / 3PM) Preventable diseases Protection against health-to-disease transition Retinal image Risk stratification

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Prediction of job characteristics for intelligent resource allocation in HPC systems:a survey and future directions

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Frontiers of computer science 2022年第5期16卷 17-33页

作者： Zhengxiong HOU Hong SHEN Xingshe ZHOU Jianhua GU Yunlan WANG Tianhai ZHAO Center for High Performance Computing School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China

Nowadays,high-performance computing(HPC)clusters are increasingly *** volumes of job logs recording many years of operation traces have been *** the same time,the HPC cloud makes it possible to access HPC services *** executing applications,both HPC end-users and cloud users need to request specific resources for different workloads by *** users are usually not familiar with the hardware details and software layers,as well as the performance behavior of the underlying HPC *** is hard for them to select optimal resource configurations in terms of performance,cost,and energy ***,how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC *** of job characteristics plays a key role for intelligent resource *** paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC *** first review the existing techniques in obtaining performance and energy consumption data of *** we survey the techniques for single-objective oriented predictions on runtime,queue time,power and energy consumption,cost and optimal resource configuration for input jobs,as well as multi-objective oriented *** conclude after discussing future trends,research challenges and possible solutions towards intelligent resource allocation in HPC systems.

关键词： high-performance computing performance prediction job characteristics intelligent resource allocation cloud computing machine learning

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A survey of cloud resource management for complex engineering applications

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Frontiers of computer science 2016年第3期10卷 447-461页

作者： Haibao CHEN Song WU Hai JIN Wenguang CHEN Jidong ZHAI Yingwei LUO Xiaolin WANG Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Institute of High Performance Computing Tsinghua University Beijing 100084 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China School of Computer and Information Engineering Chuzhou University Chuzhou 239000 China

Traditionally, complex engineering applications （CEAs）, which consist of numerous components （software） and require a large amount of computing resources, usu- ally run in dedicated clusters or high performance computing （HPC） centers. Nowadays, Cloud computing system with the ability of providing massive computing resources and cus- tomizable execution environment is becoming an attractive option for CEAs. As a new type on Cloud applications, CEA also brings the challenges of dealing with Cloud resources. In this paper, we provide a comprehensive survey of Cloud resource management research for CEAs. The survey puts forward two important questions： 1） what are the main chal- lenges for CEAs to run in Clouds？ and 2） what are the prior research topics addressing these challenges？ We summarize and highlight the main challenges and prior research topics. Our work can be probably helpful to those scientists and en- gineers who are interested in running CEAs in Cloud envi- ronment.

关键词： Cloud computing complex engineering application resource management virtualization

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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