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Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images

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arXiv 2023年

作者： Liu, Shouyue Hao, Jinkui Xu, Yanwu Fu, Huazhu Guo, Xinyu Liu, Jiang Zheng, Yalin Liu, Yonghuai Zhang, Jiong Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Cixi Biomedical Research Institute Wenzhou Medical University China School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Department of Computer Science Southern University of Science and Technology China Department of Eye and Vision Science University of Liverpool United Kingdom Department of Computer Science Edge Hill University United Kingdom

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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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

performance analysis of ACO-based improved virtual machine allocation in cloud for IoT-enabled healthcare

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Performance analysis of ACO-based improved virtual machine a...

作者： Kavitha, Kadarla Sharma, S.C. High Performance Computing Lab Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Electronics and Computer Discipline Indian Institute of Technology Roorkee Roorkee India

The Internet of Things (IoT)–enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart-patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First-Come-First-Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT-enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios. © 2019 John Wiley & Sons, Ltd.

关键词： Ant colony optimization

Fisheye Multiple Object Tracking by Learning Distortions Without Dewarping

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Fisheye Multiple Object Tracking by Learning Distortions Wit...

IEEE International Conference on Image Processing

作者： Ping-Yang Chen Jun-Wei Hsieh Ming-Ching Chang Munkhjargal Gochoo Fang-Pang Lin Yong-Sheng Chen Department of Computer Science National Yang Ming Chiao Tung University Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan Department of Computer Science University at Albany State University of New York USA College of Information Technology United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE National Center for High-Performance Computing Taiwan

We develop a new Multiple Object Tracking (MOT) scheme for fisheye cameras that can directly perform vehicle detection, re-identification, and tracking under fisheye distortions without explicit dewarping. Fisheye cameras provide omnidirectional coverage that is wider than traditional cameras, reducing fewer need of cameras to monitor road intersections. However, the problem of distorted views introduces new challenges for fisheye MOT. In this paper, we propose a Fish-Eye Multiple Object Tracking (FEMOT) approach with two novelties. We develop the Distorted Fisheye Image Augmentation (DFIA) method to improve object detection and re-identification on fisheye cameras, where fisheye model training can be performed on existing datasets of traditional cameras via fisheye data synthesis and augmentation. We also develop the Hybrid Data Association (HDA) method to perform tracking directly on fisheye views, without the need of de-warping. The developed FEMOT framework provides practical design and advancement that enables large-scale use of fisheye cameras in smart city and surveillance applications.

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A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

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arXiv 2022年

作者： Lin, Zhi Lin, Junhao Zhu, Lei Fu, Huazhu Qin, Jing Wang, Liansheng Department of Computer Science School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Nursing The Hong Kong Polytechnic University Kowloon Hong Kong

Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at https://***/jhl-Det/CVA-Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrasonic applications

Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

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arXiv 2023年

作者： Li, Heng Li, Haojin Zhao, Wei Fu, Huazhu Su, Xiuyun Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Medical Intelligence and Innovation Academy Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Hospital Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://***/liamheng/Non-IID Medical Image Segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration

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arXiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Pan, Quyang Gao, Bo Wen, Tian The State Key Lab of Processers Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer Science and Technology The Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education Beijing Jiaotong University Beijing China

The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and reduced latency. Hierarchical Federated Learning (HFL) can benefit from this paradigm by enabling multi-tier model aggregation across distributed computing nodes. However, the potential of HFL is significantly constrained by the inherent heterogeneity and dynamic characteristics of EECC environments. Specifically, the uniform model structure bounded by the least powerful end device across all computing nodes imposes a performance bottleneck. Meanwhile, coupled heterogeneity in data distributions and resource capabilities across tiers disrupts hierarchical knowledge transfer, leading to biased updates and degraded performance. Furthermore, the mobility and fluctuating connectivity of computing nodes in EECC environments introduce complexities in dynamic node migration, further compromising the robustness of the training process. To address multiple challenges within a unified framework, we propose End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration (FedEEC), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedEEC introduces two key innovations: (1) Bridge Sample Based Online Distillation Protocol (BSBODP), which enables knowledge transfer between neighboring nodes through generated bridge samples, and (2) Self-Knowledge Rectification (SKR), which refines the transferred knowledge to prevent suboptimal cloud model optimization. The proposed framework effectively handles both cross-tier resource heterogeneity and effective knowledge transfer between neighboring nodes, while satisfying the migration-resilient requirements of EECC. Extensive experiments on three datasets demonstrate that FedEEC achieves significantly hi

关键词： Federated learning