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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： Sundaresan, Vaanathi Dinsdale, Nicola K Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031830105

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%, thus ranking in the 1st place in the LISA2024 Challenge. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 IEEE.

关键词： Image denoising

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Towards LLM-driven Automated Verification of Best Practices in Digital Public Infrastructures

Towards LLM-driven Automated Verification of Best Practices ...

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2024 SICSA Workshop on Reasoning, Evaluation and Application of Large Language Models, SICSA-REALLM 2024

作者： Markovic, Milan Sripada, Somayajulu Chakrabarti, Sujit Kumar Diddigi, Raghuram Bharadwaj Department of Computing Science University of Aberdeen United Kingdom Department of Computer Science International Institute of Information Technology Bangalore India Department of Data Science and Artificial Intelligence International Institute of Information Technology Bangalore India

In this position paper, we discuss three distinct approaches for assessing risks associated with Digital Public Infrastructures (DPI) and how Large Language Models could provide automated knowledge extraction to support such analyses at scale. We further outline future research directions inspired by the domain of collective intelligence and formal verification methods. © 2024 Copyright for this paper by its authors.

关键词： Digital Public Infrastructures Formal Verification Infrastructure Analysis Large Language Model

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Assessing a Personalized, Hybrid, and Generic Approach for Glucose Prediction in Type 1 Diabetes

Assessing a Personalized, Hybrid, and Generic Approach for G...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bosoni, Pietro Jensen, Morten Hasselstrom Bellazzi, Riccardo Cichosz, Simon Lebech University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy Aalborg University Data Science Department of Health Science and Technology Aalborg Denmark Aalborg University Department of Health Science and Technology Aalborg Denmark

ISBN: (纸本)9798350386226

This study investigates the potential of using generic, hybrid, and personalized neural network models for glucose prediction in individuals with Type 1 Diabetes (T1D). data from 194 participants in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, totaling over 46 million minutes of Continuous Glucose Monitoring (CGM), were used to develop and evaluate the models. A baseline reference model, Last Observation Carried Forward (LOCF), was also included for comparison. Models were trained using data from 70% of the participants and tested on the remaining 30%, with prediction horizons (PH) set at 30 and 60 minutes. At the 30-minute PH, the generic model achieved a Root Mean Square Error (RMSE) of 19.6 mg/dL and a Mean Absolute Relative Difference (MARD) of 9.2%. These results were slightly worse than those of the hybrid and personalized models, which yielded RMSEs of 19.4 mg/dL and 19.5 mg/dL, respectively, and MARDs of 9.6% for both. However, the differences were not statistically significant. At the 60-minute PH, the generic model showed the best performance, with an RMSE of 34.9 mg/dL and MARD of 16.2%. The hybrid and personalized models exhibited slightly higher RMSEs (35.3 mg/dL and 35.5 mg/dL, respectively) and MARDs (17.8% and 18.1%, respectively). These findings suggest that both generic and individualized models can provide satisfactory glucose forecasting results based solely on CGM data. Nonetheless, the potential benefits of individualized approaches deserve further investigation, particularly when substantial training data are available. © 2024 IEEE.

关键词： Prediction models

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Towards property-preserving JPEG encryption with structured permutation and adaptive group differentiation

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Visual computer 2024年第9期40卷 6421-6447页

作者： Su, Guo-Dong Chang, Ching-Chun Lin, Chia-Chen Chang, Chin-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Fujian Province University Fuzhou China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cryptography

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Classification of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Swin Transformer

Classification of Tuberculosis Bacilli from Microscopic Sput...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vallabhaneni, Praneeth Satapathy, Ashutosh Indugula, Manisha Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331509828

Tuberculosis (TB) has been a great challenge in the health world, and proper treatment requires proper diagnosis at the right time. This paper has classified the bacilli in sputum samples into single/simple and clump formations using deep-learning transformer techniques. The method starts with semantic segmentation, where TransUNet model has been used on sputum images to detect regions containing the bacilli. The superior visual task performer, the Swin Transformer, further classifies the regions identified. Accuracy, precision, recall, F1-score, and AUC-ROC are the metrics used to evaluate the model's performance. This classification contributes to TB diagnosis by quantifying bacterial load and categorizing infection severity. This type of differentiation may guide clinicians in developing more precise treatment strategies as the variability of infection severity may be observed in patients suffering from TB. Integrating the recent transformer models, such as Swin Transformer and TransUNet, into the microbiological diagnosis process will improve the speed of TB diagnosis, and automated bacterial classification will occur through an organized process. This study will help diagnose TB by improving the capacity of diagnosis, which subsequently helps in better decision-making regarding treatment and management, independent of human interpretation. The successful classification of TB bacilli resulted in an average accuracy of 93%, average precision of 93%, average recall of 92.5%, and average F1-score of 92.5%. © 2025 IEEE.

关键词： Semantic Segmentation

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python 1

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16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023

作者： Melgar-García, Laura Troncoso-García, Ángela Gutiérrez-Avilés, David Torres, José Francisco Troncoso, Alicia Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville Spain

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a computer science degree where the programming language changed from Octave to Python. Experiments are performed to analyze the explainability using the SHapley Additive exPlanations algorithm for XGBoost regressor model (for numerical grade prediction) and XGBoost classifier model (for class grade prediction). After the validation and training process, several conclusions are drawn that validate the idea of changing the programming language to a more popular one such as Python. For example, regarding classification problems, the most important feature for the insufficient class in the Octave courses is the practical exam. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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