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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Dhar, Prasenjit Singh, Mohit Kumar Ahmed, Tanveer Suganya, Devi K. Medical Imaging Laboratory Computer Science and Engineering Department National Institute of Technology Silchar Assam India

ISBN: (纸本)9789819768011

Red blood cells (RBCs) also known as erythrocytes are an essential component of the human body that play several important roles. RBCs transport oxygen (O2) from the lungs to the body’s tissues, remove carbon dioxide (CO2), regulate pH balance, support the immune system, and provide diagnostic information. The abnormal shapes of RBCs (signified as poikilocytosis) are unable to carry O2 and CO2, leading to decreased O2 delivery to the tissues and an increased workload on the heart and lungs. Anemia, thalassemia, and other blood-related illnesses affect the body for insufficient replenishment (oxygen, protein, nutrients). Hematologists take more time to examine RBC shapes manually using a microscope. In this study ensemble deep learning technique was introduced to examine poikilocytosis abnormality accurately and efficiently. The proposed ensemble model outperforms state-of-the-art approaches with an excellent accuracy and F1-score of 98.77%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Detector with Classifier2: An end-to-end Multi-Stream Feature Aggregation Network for Fine-Grained Object Detection in Remote Sensing Images

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IEEE Transactions on Image Processing 2025年 34卷 2707-2720页

作者： Zheng, Shangdong Wu, Zebin Xu, Yang He, Chengxun Wei, Zhihui Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense China

Fine-grained object detection (FGOD) fundamentally comprises two primary tasks: object detection and finegrained classification. In natural scenes, most FGOD methods benefit from higher instance resolution and fewer environmental variation, attributing more commonly associated with the latter task. In this paper, we propose an unified paradigm named Detector with Classifier2 (DC2), which provides a holistic paradigm by explicitly considering the end-to-end integration of object detection and fine-grained classification tasks, rather than prioritizing one aspect. Initially, our detection sub-network is restricted to only determining whether the proposal is a coarse-category and does not delve into the specific sub-categories. Moreover, in order to reduce redundant pixel-level calculation, we propose an instance-level feature enhancement (IFE) module to model the semantic similarities among proposals, which poses great potential for locating more instances in remote sensing images (RSIs). After obtaining the coarse detection predictions, we further construct a classification sub-network, which is built on top of the former branch to determine the specific sub-categories of the aforementioned predictions. Importantly, the detection network is performed on the complete image, while the classification network conducts secondary modeling for the detected regions. These operations can be denoted as the global contextual information and local intrinsic cues extractions for each instance. Therefore, we propose a multi-stream feature aggregation (MSFA) module to integrate global-stream semantic information and local-stream discriminative cues. Our whole DC2 network follows an end-to-end learning fashion, which effectively excavates the internal correlation between detection and finegrained classification networks. We evaluate the performance of our DC2 network on two benchmarks SAT-MTB and HRSC2016 datasets. Importantly, our method achieves the new state-of-theart results com

关键词： Object detection

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Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT Systems 24th

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Dee...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Tang, Fengjie Xu, Jia Liu, Xiao Yao, Aiting Fang, Mingyan Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9789819615414

Artificial Intelligence of Things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have different task priorities and heterogeneous resource requirements. However, these factors will affect the QoS of the solution and the task response time. Therefore, motivated by a typical smart warehouse system, a Multi-access Edge Computing (MEC) based distributed task offloading framework is proposed. Afterward, a delay-sensitive mixed-integer programming model is defined to address the imbalanced utilization of computing resources between edge and cloud servers during the offloading process. Finally, based on the attention mechanism, a Priority-Driven Multi-Agent Deep Reinforcement Learning algorithm (PDMA-DRL) is proposed to fulfill real-time resource requirements while reducing the task response time. The PDMA-DRL algorithm consists of two agents, and each agent corresponds to a sub-problem. Comprehensive experimental results demonstrate that our proposed PDMA-DRL algorithm can outperform representative existing methods in generating delay-sensitive task offloading plans and effectively reducing the task response time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical imaging 2025年第5期44卷 2295-2306页

作者： Huang, Kaiwen Zhou, Tao Fu, Huazhu Zhang, Yizhe Zhou, Yi Gong, Chen Liang, Dong Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Institute of High-Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southeast University School of Computer Science and Engineering Nanjing211189 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Research Center for Medical AI Shenzhen518055 China

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM’s predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. © 1982-2012 IEEE.

关键词： Self-supervised learning

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A Graph Structure-Feature Learning Network for Diagnosing Alzheimer’s Disease Based on Multi-modal Brain Biometric Feature 18th

A Graph Structure-Feature Learning Network for Diagnosing A...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Shang, Dongxu Wang, Huabin Zhang, Mengxin Peng, Yuhang Ye, Xingjian Wang, Zilin Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui Hefei China Stony Brook Institute at Anhui University Anhui Hefei China

ISBN: (纸本)9789819610709

Alzheimer’s Disease (AD) is a neurodegenerative disease, typically identified and diagnosed through brain biometric feature. However, certain common brain biometric feature exist among patients in different stages of AD, which result in incorrect edges within the graph structure. To address above issues, this paper proposes a Graph Structure-Feature Learning Network (GSFLN) model. The model simultaneously learns a graph structure that appropriately represents the relationships among patients and selects the significant pathogenic brain biometric feature of patients. Firstly, a Graph Convolutional Clustering (GCC) module is designed to capture the relationships between non-image data of patients. Secondly, a Pathogenic Feature Selection (PFS) module is designed to capture the relationships between image data of patients and select the pathogenic brain biometric feature. Thirdly, an Unsupervised Predictive Clustering (UPC) module is designed to control the above two modules. Our proposed model has surpassed the current state-of-the-art level in diagnostic performance on both the NACC and Tadpole datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s Disease brain biometric feature Feature Selection Graph Convolutional Network Graph Structure Learning

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NETrack: A Lightweight Attention-Based Network for Real-Time Pose Tracking of Nasal Endoscope Based on Endoscopic Image 21st

NETrack: A Lightweight Attention-Based Network for Real-Tim...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Shao, Liangjing Chen, Benshuang Chen, Xinrong Academy for Engineering and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory for Medical Imaging Computing and Computer Assisted Intervention Fudan University Shanghai200032 China

ISBN: (纸本)9789819601271

Vision-based real-time pose tracking of endoscope is significant for navigation and automation of endoscopy. In this work, a deep learning-based framework is proposed for vision-based real-time pose tracking of nasal endoscope based on endoscopic images. Specifically, an attention-based module is introduced into the feature encoder to extract and integrate multi-dimensional and multi-scale information. Furthermore, a novel pose encoder is proposed to extract more complicated representation from the feature map for pose vector prediction. The proposed method is compared with state-of-the-art methods on the dataset, which consists of endoscopic videos and endoscope poses obtaining by an optical tracking system. The experimental results demonstrate that the proposed network achieves better performance than state-of-the-art works for real-time nasal endoscope pose estimation. The average tracking error of our method is 5.78 mm, while the relative translation error and rotation error are respectively 0.48 mm and 0.22 degree on average. In addition, the average inference speed of our network is 46 fps, which achieves the real-time requirement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Endoscopy

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An Ensemble-Based Approach for Smart Detection of Heart Diseases Symptoms 3rd

An Ensemble-Based Approach for Smart Detection of Heart Dise...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Balaji, Prasanalakshmi Bansal, Shivam Banerjee, Subham Mishra, Sushruta Sridevi, R. Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Medical Imaging Research Lab Research Associate SCRA Chennai India

ISBN: (纸本)9789819726707

Machine learning is widely used in health care. Heart disease is the world's most dangerous chronic life-threatening disease, and with heart disease rates on the rise, we need to set up a system that can detect and prevent heart disease symptoms early on. Various and many algorithms in machine learning are being used to predict the early symptoms of coronary heart disease. But the majority of those suffer from low accuracy rates. In this particular study, an ensemble-based approach is utilized to train the model using the dataset on heart diseases obtained from the UCI repository. The model captures various heart disease data from different sensors and then uses an ensemble-based random forest algorithm to accurately detect the symptoms pertaining to heart disease risks in patients. The model’s accuracy is quite satisfactory at 93.8% with the use of 40 decision trees in random forest. Thus, the proposed model can serve as a reliable tool for medical experts in detecting heart disease symptoms with greater accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Healthcare Heart disease Internet of Things Machine learning Random forest

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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