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Axis-Guided Quality Assessment and Multi-label Hippocampal and Ventricular Segmentation in Low-Resolution Pediatric Brain MRI 1st

Axis-Guided Quality Assessment and Multi-label Hippocampal a...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Kim, Hyunwook Seo, Jinew Ryu, Seiyoung Park, Joon hyung On, Sungchul Choi, Jinwha Department of Convergence Medicine Asan Medical Institute of Convergence Science and Technology University of Ulsan College of Medicine Asan Medical Center 388-1 Pungnap2-Dong Songpa-Gu Seoul Korea Republic of Department of Biomedical Engineering BK21 Project Asan Medical Center University of Ulsan College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031830105

The Swoop system of Hyperfine Inc. is an affordable, ultra-low-field MRI developed for use in a clinical setting. However, despite its advantages, the relatively low resolution of 64mT MRI data poses additional challenges, especially in examining small structures such as the hippocampus or vessels. As a part of our attempt at the Low field pediatric brain magnetic resonance Image Segmentation and Quality Assurance (LISA) Challenge 2024, we developed two deep learning-based models. First, to evaluate the image quality of 64mT T2 brain MRI data, we implemented an axis classifier module to improve the model performance. Second, for segmentation of the hippocampus in the MRI, a multi-label learning method was used for more accurate segmentation. With these models, we expect to alleviate the accessibility barrier to brain MRI. © The Author(s) 2025.

关键词： Pediatrics

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Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction 44th

Structured Radial Basis Function Network: Modelling Diversit...

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44th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Dominguez, Alejandro Rodriguez Shahzad, Muhammad Hong, Xia Department of Computer Science University of Reading Reading United Kingdom

ISBN: (纸本)9783031779145

Multi-modal problems can be effectively addressed using multiple hypothesis frameworks, but integrating these frameworks into learning models poses significant challenges. This paper introduces a Structured Radial Basis Function Network (s-RBFN) as an ensemble of multiple hypothesis predictors for regression. During the training of the predictors, first the centroidal Voronoi tessellations are formed based on their losses and the true labels, representing geometrically the set of multiple hypotheses. Then, the trained predictors are used to compute a structured dataset with their predictions, including centers and scales for the basis functions. A radial basis function network, with each basis function focused on a particular hypothesis, is subsequently trained using this structured dataset for multiple hypotheses prediction. The s-RBFN is designed to train efficiently while controlling diversity in ensemble learning parametrically. The least-squares approach for training the structured ensemble model provides a closed-form solution for multiple hypotheses and structured predictions. During the formation of the structured dataset, a parameter is employed to avoid mode collapse by controlling tessellation shapes. This parameter provides a mechanism to balance diversity and generalization performance for the s-RBFN. The empirical validation on two multivariate prediction datasets—air quality and energy appliance predictions—demonstrates the superior generalization performance and computational efficiency of the structured ensemble model compared to other models and their single-hypothesis counterparts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radial basis function networks

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Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edge Detection and Deep Learning 27th

Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edg...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Vu, Ha Anh Vajda, Szilárd Department of Computer Science Central Washington University EllensburgWA98926 United States

ISBN: (纸本)9783031782008

Brain tumor classification from MRI scans demands precise image analysis, a challenge compounded by the variable morphology and location of tumors. Addressing this, our study presents an innovative approach that combines edge detection with a hierarchical deep learning framework to classify brain tumors accurately. This method enhances edge clarity, facilitating the deep learning model’s ability to distinguish between meningioma, glioma, and pituitary tumors. By deploying a two-stage model, initially segregating a meta-tumor class and pituitary and subsequently refining the meta-tumor class into glioma and meningioma with a binary classifier, we capitalize on the strengths of both traditional image processing and advanced neural networks. The already proven ResNet50 architecture, our model’s backbone, benefits from transfer learning, enabling efficient feature extraction from the edge image tailored to brain tumor recognition. Our results, evidenced by an over 96% overall accuracy rate obtained on a large benchmark brain tumor dataset, underscore the potential of integrating edge detection processing with deep learning. This integrative multi-level strategy promises to streamline the diagnostic process, offering a reliable, fast, and cost-effective solution that could reduce the need for expensive human specialist intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Detection and Tracking of Pinus Radiata Catkins 1

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Song, Eric Schofield, Sam Green, Richard Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9783031258251

ISBN: (纸本)9783031258244

Pinus Radiata trees form pollen-producing catkins that can be harvested for pharmaceutical uses. Unmanned Aerial Vehicles (UAVs) may be well suited to the task of autonomously harvesting these catkins. We propose a method to reliably detect and track P. Radiata catkins in three dimensions that can be used for real-time guidance of a UAV. The method applies the YOLOv5 deep learning algorithm to detect catkins in the X-Y plane. A novel optimisation of the MeanShift algorithm is utilised to assist existing contour detection algorithms in segmenting individual catkins in the Z plane. A Kanade-Lucas-Tomasi tracker was used with RANSAC for accurate frame-to-frame tracking. The method achieved a Mean Average Precision of 0.87 on images taken at a commercial pine pollen farm. The method detected the depth of catkins at distances of up to 1200 mm to an accuracy of 2 mm, or 8 mm for occluded catkins. Detected catkins can be reliably tracked at speeds of 1ms. An average frame rate of 22 frames per second was achieved on an Intel i5 CPU, with the Meanshift optimisation performing up to 41 times faster than existing implementations. These results indicate that the proposed method could be used to successfully assist in the automated harvesting of P. Radiata catkins. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Ponzi Scheme Detection and Prevention in Blockchain Platforms Using Machine Learning: A Systematic Literature Review 26th

Ponzi Scheme Detection and Prevention in Blockchain Platfo...

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26th International Conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Castro Severiche, Karen Esther Wahlqvist Odenman, Agnes Jalali, Amin Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9783031780899

A Ponzi scheme is an investment fraud in which existing investors are paid with funds collected from new investors, which causes significant financial losses. This fraudulent activity also exists in blockchain-enabled platforms, but it can be detected and prevented through the application of machine learning techniques. This paper aims to identify and report solutions to detect and prevent Ponzi schemes on blockchain-enabled platforms. The research follows a Systematic Literature Review methodology following the PRISMA 2020 guidelines. The data collected during this study was recorded and stored in the Open science Framework, ensuring transparency, reproducibility, and supporting future research endeavors. In the end, 49 papers were identified through a process of screening and snowballing. These papers are further studied to report publication trends, applied algorithms, and the reported challenges. The findings indicate a rising global trend in the use of machine learning to detect and prevent Ponzi schemes, particularly since 2017, with China and India leading the way. Ethereum and Bitcoin are the most frequently utilized platforms, while the combination of Support Vector Machine, Random Forest, and Extreme Gradient Boosting emerges as the effective approach. Data imbalance, data quality issues, and computational limitations are identified as key challenges in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman School of Computer Science The University of Sydney Sydney Australia Med-X Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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OpenVenus: An Open Service Interface for HPC Environment Based on SLURM 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Wan, Meng Cao, Rongqiang Wang, Yangang Wang, Jue Li, Kai Wang, Xiaoguang Yang, Qinmeng Computer Network Information Center Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the emergence of more and more "AI + Field + HPC" applications, it is urgent to solve the problem of scheduling and management of High-Performance Computing (HPC) resources, as well as the fast and efficient "cloud service" of HPC applications. This engineering problem is particularly critical because it affects the progress of scientific research, the development period of the research platform, and the learning cost of scientists. To solve the problem, a set of reusable life cycle processes for HPC resources are designed. Based on the life cycle, we propose an open service interface based on HPC, which reduces the startup time under multiple refreshes and abnormal retries by using the mode of contention lock. The active interruption of users is a typical scenario in the startup phase. Furthermore, a read-write strategy with an overlay based on Singularity is implemented to save storage space and improve running speed. In order to evaluate the serviceability and performance of the proposed interface, we deploy the service on the Venus platform and make a startup comparison experiment. In addition, the reduction of storage for 100 users is also tested. The experimental results show that under the HPC environment with SLURM, the proposed open-service interface can effectively shorten 46% startup time of applications and services and reduce 25% storage at least for each user of the Venus platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer software reusability

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification 6th

An Evolutionary Approach to Automated Class-Specific Data A...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Marc, Silviu Tudor Belavkin, Roman Windridge, David Gao, Xiaohong Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom

ISBN: (纸本)9783031503191

Convolutional neural networks (CNNs) can achieve remarkable performance in many computer vision tasks (e.g. classification, detection and segmentation of images). However, the lack of labelled data can significantly hinder their generalization capabilities and limit the scope of their applications. Synthetic data augmentation (DA) is commonly used to address this issue, but uniformly applying global transformations can result in suboptimal performance when certain changes are more relevant to specific classes. The success of DA can be improved by adopting class-specific data transformations. However, this leads to an exponential increase in the number of combinations of image transformations. Finding an optimal combination is challenging due to a large number of possible transformations (e.g. some augmentation libraries offering up to sixty default transformations) and the training times of CNNs required to evaluate each combination. Here, we present an evolutionary approach using a genetic algorithm (GA) to search for an optimal combination of class-specific transformations subject to a feasible time constraint. Our study demonstrates a GA finding augmentation strategies that are significantly superior to those chosen randomly. We discuss and highlight the benefits of using class-specific data augmentation, how our evolutionary approach can automate the search for optimal DA strategies, and how it can be improved. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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