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ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation 1st

ECLNet: Efficient Convolution with Lite Transformer for 3D M...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Zheng, Lei Zhang, Yuzhong Chen, Huagui Guo, Yuheng Zhou, Lei School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Attrsense Shangha Shanghai China Shanghai Waigaoqiao Shipbuilding and Offshore University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9789819633487

Convolutional neural networks (CNNs) have been significantly advanced in the field of medical imaging in recent years. The implementation of U-shaped architectures has proven particularly effective, demonstrating exceptional results across various image segmentation challenges. Despite these achievements, convolutional operations face challenges in capturing broad-scale global context and long-distance semantic relationships. Therefore, the ECLNet is presented in this paper, a converter with an architecture similar to U-Net that was specifically designed for medical image segmentation. With the help of the Transformer architecture, complete global context is extracted by this model through the processing of feature maps produced by the CNN as input sequences. To achieve accurate feature localization, the CNN feature map is combined with the global context information. It is claimed that the Transformer design can be utilized as a reliable encoder for medical image segmentation applications. Its combination with convolutional networks enhances segmentation efficiency by facilitating the restoration of local spatial information. Finally, extensive experiments on one public and one private datasets show that our proposed algorithm performs better than several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model 5th

Deep Learning-Based Cellular Nuclei Segmentation Using Trans...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Erezman, Mateusz Dziubich, Tomasz Computer Vision and Artificial Intelligence Laboratory Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031704208

Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041. The obtained results seem to be clinically relevant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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10th Balkan conference in Informatics, BCI 2024

10th Balkan Conference in Informatics, BCI 2024

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10th Balkan conference in Informatics, BCI 2024

ISBN: (纸本)9783031840920

The proceedings contain 23 papers. The special focus in this conference is on Informatics. The topics include: Unveiling the Potential of Explainable Artificial Intelligence in Predictive Modeling, Exploring Food Security and Nutrition in Madagascar;Comparison of Methodological Approaches: CRISP-DM vs OSEMN Methodology Using Linear Regression and Statistical Analysis;processing Range-Sum Queries Within Triangular Shapes;microservice-Based Distributed Application for Unifying Social Networks;towards an Ontology for Describing the Contextualization of Written Cultural Heritage in the Light of Semiotics;affective Analysis of Literature Books - Detective Novels and Short Stories of Agatha Christie;preventing Academic Dishonesty Originating from Large Language Models;a Gamified Approach for Monitoring Student Attendance and Fostering Learner Engagement: AttendanceManager Platform;personalized Online Learning Based on IoT (Systematic Mapping Study);Student Perspectives on Generative AI Use in Higher Education: The Automation and Augmentation of Learning;leveraging Fitness Functions to Assess Cloud Migration Readiness of Clean Architecture;resource Management in Cloud IaaS via Machine Learning Algorithms;blockchain-Based Decentralised Marketplace for Secure Trading of Intellectual Property;an Intelligent Agent-Based Approach to Enhancing Urban Health and Resilience under Climate Change;MAS-PatientCare: Medical Diagnosis and Patient Management System Based on a Multi-agent Architecture;Microsoft Copilot, Google Gemini, OpenAI ChatGPT and Anthropic Claude: Assisting in Cardiology Diagnosing - A Case Study;enhancing Web Accessibility Through a Customizable Browser Extension for Users with visual Impairments;time Traveller - A RotSafeLinker Tool;shareFactory - Efficient and Accessible Content-Sharing Platform;steganalysis: A Study on Compression Attack, Methods and Transit Resilience;a Practical Guide for Application Security Baselines in the Software Development Process.

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NEURAHOLO: A System for High-Resolution 3D Human Digitization on Holograms 2nd

NEURAHOLO: A System for High-Resolution 3D Human Digitizatio...

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2nd International conference on Intelligent Systems and Data Science, ISDS 2024

作者： Nguyen, Hoang Pham Ngoc, Thai Anh Huynh Le Gia, Luat Le Gia, Khoi College of Information and Communication Technology- Can Tho University Can Tho Viet Nam

ISBN: (纸本)9789819796151

Amid the growing prominence of Holographic Projection technology in Vietnam’s technology trend, we introduce NEURAHOLO: an innovative system designed to convert 2D images into 3D models for holographic displays. The system compares two advanced image processing algorithms for background removal U2-net and Mediapipe Selfie Segmentation followed by PIFuHD for 3D model generation, and a 3D Led Holodisplay for visualization. Our evaluation on the Supervisely Person Dataset with over 1000 images resulted in an IoU score of 0.9309 for U2-net, outperforming Mediapipe Selfie Segmentation, which scored 0.8467. The Chamfer Distance was used to measure the accuracy of the 3D models, with an average error of just 0.986, thereby corroborating the system’s adeptness at precise 3D model re-creation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Comparison of On-Board and Off-Board processing Power Consumption for Drone Camera images 16th

Comparison of On-Board and Off-Board Processing Power Cons...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Mitrev, Atanasko Boris Stojkoska, Biljana Risteska Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031861611

Unmanned aerial vehicles (UAVs) equipped with cameras, capture images and videos from high altitude, usually for surveillance purpose. This paper investigates the power consumption of a drone processing unit under two different operational scenarios: (i) On-Board (local image processing using an onboard Raspberry Pi) and (ii) Off-Board (streaming video over Wi-Fi for remote processing). Therefore, we deployed the well-known YOLO (You Only Look Once) image detection algorithm on the Raspberry Pi and investigated the power consumption for the both scenarios. Contrary to the expectations, our findings prove that energy consumption is very similar in both scenarios. The results of this study are valuable for designers of energy-efficient UAV systems, particularly for applications where prolonged flight duration and real-time data processing are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis 1st

Application of Classification Framework Based on CDR and CNN...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Xu, Yunjiao Yang, Jie Guo, Tianjiao Scientific Research Office Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

The seven-field classification plays a key role in ophthalmology, especially in the diagnosis and treatment of diabetic retinopathy. In this paper, we designed a framework that can classify 7 fields of each eye simultaneously, and applied it to the medical-assisted interactive system of 7 fields of classification。F1 and F2 field of view are detected through a combination of algorithms based on decision rules and convolutional neural networks. The total accuracy on the internal test set is 98.7%(95%CI, 98.4%–99.1%). In external tests, the total accuracy of the algorithm in this paper on the LONGITUDINAL data set is 98.6%(95%CI,97.9%–99.3%). The total accuracy on the Drishti-GS data set is 100%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Integrating Deep Learning and Imaging Techniques for High-Precision Brain Tumor Analysis 2nd

Integrating Deep Learning and Imaging Techniques for High-Pr...

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2nd International conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024

作者： Gokapay, Dilip Kumar Mohanty, Sachi Nandan Andhra Pradesh Amaravati India

ISBN: (纸本)9783031813689

The brain, as the core organ of the human nervous system, governs a wide range of bodily functions. These include vital processes such as breathing and regulating heart rate, as well as higher-order functions like thinking, emotional responses, and creativity. Composed of billions of neurons and glial cells, the brain is divided into distinct regions, each specializing in specific functions. Neurons in the brain communicate through an intricate system of electrical and chemical signals, facilitating information processing, memory storage, and responses to external stimuli. Investigating brain tumors is a significant aspect of medical research that aims to enhance the accuracy of diagnoses, treatment techniques, and patient outcomes. Using cutting-edge machine learning algorithms on data collected from imaging modalities like MRI and CT scans for the purpose of tumor identification, classification, and segmentation is the main focus of this research. Combining conventional machine learning techniques with convolutional neural networks (CNNs) substantially enhances the processing and interpretation of complex medical pictures. The study also focuses on developing a comprehensive dataset, employing preprocessing techniques to enhance image clarity, and applying deep learning models to accurately detect tumor locations. We use several performance indicators to evaluate the models’ efficacy, ensuring they meet clinical requirements. Our results demonstrate a significant improvement over earlier methods in brain tumor identification, with a success rate of 95.35%. This program will assist healthcare professionals, including radiologists, in making more informed decisions, which should lead to improved outcomes for patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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EdgCNN: Thangka Line Drawing Extraction Based on CNN 19th

EdgCNN: Thangka Line Drawing Extraction Based on CNN

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19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Song, Meihua Zhang, Dan Zhang, Jianpeng Wang, Ning Peng, Quanhong Xu, Chenhao Computer College of Qinghai Normal University Xining81017 China State Key lab of Tibetan Intelligent Informntion Processing and Application Co-established by province and ministry Xining81017 China

ISBN: (纸本)9789819799183

In the process of creating Thangka paintings, the same line drawing can be used to produce different types of Thangka, but each type requires the artist to redraw an identical line drawing. This step is labor-intensive and time-consuming, relying heavily on manual work. Due to the difficulties in obtaining real line drawing images of Thangka and the distortion issues present in existing line drawing extraction methods, this paper proposes a novel Thangka line drawing extraction method based on Convolutional Neural Networks (CNN) combined with edge detection, named EdgCNN. This method incorporates innovative edge detection and line drawing fine-tuning modules, improving the accuracy and quality of line drawing extraction and effectively removing image noise, producing smooth lines and detailed Thangka line drawings. We conducted experiments on the Thangka1500 dataset, and comprehensive evaluation results indicate that our method outperforms existing methods in Thangka manuscript image extraction. Therefore, our method provides new technical support for the digital creation of Thangka art and the preservation of intangible cultural heritage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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XcUNet for Skin Lesion Segmentation Followed by Clustering Based Post-processing 4th

XcUNet for Skin Lesion Segmentation Followed by Clustering B...

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4th International conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Verma, Neetu Ranvijay Yadav, Dharmendra Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh Prayagraj India

ISBN: (纸本)9783031837951

Human cancer is one of the worst illnesses and is mainly caused by inherited instability and a variety of molecular disruptions. Worldwide, skin cancer is one of the most common types of cancer, and patient survival depends on an early and precise diagnosis. Although it is crucial, clinical evaluation of skin lesions is fraught with difficulties, including significant wait times and individual interpretations. Dermatologists have difficulties in early skin cancer detection, and deep learning has recently been widely used in supervised and unsupervised learning tasks. Defining the lesion region is crucial to the effectiveness of these techniques because it allows for accurate segmentation of skin cancer lesions at various stages, which helps in early diagnosis and treatment. In this study, we have proposed an Xception model-based U_Net (XcUNet) model for skin lesion image segmentation. The proposed model’s lower computational load from separable convolutions and residuals usually results in better detection times and more sophisticated comprehension of spatial hierarchies and patterns. This attribute is extremely important in clinical fields where quick outcomes are required, such as skin lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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