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Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

arXiv

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arXiv 2025年

作者： Yao, Ni Liu, Xiangyu Sun, Danyang Han, Chuang Li, Yanting Nan, Jiaofen Li, Chengyang Zhu, Fubao Zhou, Weihua Zhao, Chen School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://***/chenzhao2023/MGFA_CCTA. © 2025, CC BY.

关键词： Image segmentation

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Discovery of transient topological crystalline order in optically driven SnSe

arXiv

引用

arXiv 2025年

作者： Mogi, Masataka Choi, Dongsung Oh, Kyoung Hun Golovanova, Diana Zhao, Yufei Su, Yifan Shen, Zongqi Azoury, Doron Xia, Haoyu Ilyas, Batyr Luo, Tianchuang Kida, Noriaki Osaka, Taito Togashi, Tadashi Yan, Binghai Gedik, Nuh Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Applied Physics University of Tokyo Bunkyo-ku Tokyo Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany Department of Condensed Matter Physics Weizmann Institute of Science Rehovot Israel RIKEN SPring-8 Center Hyogo Sayo Japan Japan Synchrotron Radiation Research Institute Hyogo Sayo Japan

Ultrafast optical excitation of quantum materials has opened new frontiers for transiently inducing novel phases of matter1, including magnetism2, charge density waves3, ferroelectricity4,5, and superconductivity6 beyond the constraints of equilibrium thermodynamics. Triggering a transient topological order in a trivial semiconductor represents a key milestone, as it could provide an on-demand route to topological functionality for device applications. However, achieving a topologically nontrivial phase from a large-gap (~ 1 eV) semiconductor remains a major challenge, as substantial energy modification is required to invert the band gap. Here, we report the discovery of a thermally inaccessible, transient topological crystalline order in a sizable-gap (~ 0.8 eV) layered semiconductor, SnSe, through femtosecond above-gap excitation. Time- and angle-resolved photoemission spectroscopy reveals a Dirac-like linear dispersion forming within the band gap on a subpicosecond timescale. This transient state shows hallmark features of a reflection-invariant topological crystalline insulator7, including a high Fermi velocity (2.5´105 m/s), multiple Dirac points located away from high-symmetry momenta, and independence from probe photon energy, persisting for several picoseconds even at room temperature. Our findings establish a nonequilibrium pathway to ultrafast topological order in a semiconductor, opening new avenues for optically driven spintronic and quantum information technologies. © 2025, CC BY.

关键词： Charge density waves

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

引用

Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper.

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Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques

Natural Language Processing Journal

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Natural Language Processing Journal 2025年 11卷

作者： Juswin Sajan John Boppuru Rudra Prathap Gyanesh Gupta Jaivanth Melanaturu Department of Computer Science and Engineering CHRIST UNIVERSITY Bengaluru India Department of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data. The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM. The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress. Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model’s superior average performance, it exhibited higher variability among different labels.

关键词： Mental stress Natural language processing Machine learning Deep learning Mental health research LLM

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A Lightweight Deep Convolutional Neural Network with Squeeze-and-Excitation Modules for Efficient Human Activity Recognition using Smartphone Sensors 2

A Lightweight Deep Convolutional Neural Network with Squeeze...

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2nd International Conference on Big Data Analytics and Practices, IBDAP 2021

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665428415

Human activity recognition (HAR) relying on wearable sensors has become a new challenging area of research in pervasive and ubiquitous computing due to the rapid advancements in sensor technology. Since several deep learning (DL) networks have been introduced to handle the problem of feature extraction in machine learning, these techniques have recently garnered considerable attention. Nevertheless, most recent DL networks utilize sensory data by automatically extracting spatial properties without addressing cross-channel data at the same level. This information from each sensor channel was independently conveyed in a hierarchical method from shallow levels to deeper levels. This paper introduces SE-DeepConvNet, a lightweight deep convolutional neural network with squeeze-and-excitation modules for recognizing human activity from smartphone sensor data. The recommended SE-DeepConvNet was developed and assessed on the UCI-HAR dataset, a public benchmark HAR dataset. According to the results obtained, the SE-DeepConvNet outperforms other baseline DL networks with a maximum accuracy of 99.27%. © 2021 IEEE.

关键词： Smartphones

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Ansatz-free Hamiltonian learning with Heisenberg-limited scaling

arXiv

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arXiv 2025年

作者： Hu, Hong-Ye Ma, Muzhou Gong, Weiyuan Ye, Qi Tong, Yu Flammia, Steven T. Yelin, Susanne F. Department of Physics Harvard University CambridgeMA02138 United States Department of Electronic Engineering Tsinghua University Beijing China School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Quantum Information IIIS Tsinghua University Beijing China Department of Mathematics Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Computer Science Virginia Tech AlexandriaVA22314 United States Phasecraft Inc. WashingtonDC United States

Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensing. The problem, known as Hamiltonian learning, is well understood under the assumption that interactions are local, but this assumption may not hold for arbitrary Hamiltonians. Previous methods all require high-order inverse polynomial dependency with precision, unable to surpass the standard quantum limit and reach the gold standard Heisenberg-limited scaling. Whether Heisenberg-limited Hamiltonian learning is possible without prior assumptions about the interaction structures, a challenge we term ansatz-free Hamiltonian learning, remains an open question. In this work, we present a quantum algorithm to learn arbitrary sparse Hamiltonians without any structure constraints using only black-box queries of the system’s real-time evolution and minimal digital controls to attain Heisenberg-limited scaling in estimation error. Our method is also resilient to state-preparation-and-measurement errors, enhancing its practical feasibility. Moreover, we establish a fundamental trade-off between total evolution time and quantum control on learning arbitrary interactions, revealing the intrinsic interplay between controllability and total evolution time complexity for any learning algorithm. These results pave the way for further exploration into Heisenberg-limited Hamiltonian learning in complex quantum systems under minimal assumptions, potentially enabling new benchmarking and verification protocols. © 2025, CC BY.

关键词： Hamiltonians

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Detection of Freezing of Gait in Parkinson's Disease by Squeeze-and-Excitation Convolutional Neural Network with Wearable Sensors 15

Detection of Freezing of Gait in Parkinson's Disease by Sque...

引用

15th International Conference on Open Source Systems and Technologies, ICOSST 2021

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665413053

It is one of the most severe motor indications of Parkinson's disease that one's stride becomes freezing of gait (FOG). Patients' quality of life is negatively impacted by FOG, which may lead to falls. Typically, questionnaires have been used to diagnose FOG;however, this method is subjective and may not correctly represent the severity of this disorder. It is possible to monitor symptoms using sensor-based devices, which can provide reliable and objective data. In this paper, the SE-DeepConvNet, a compact deep convolutional neural network including squeeze-and-excite components for fog detection, was proposed. In conducted to evaluate SE-DeepConvNet, we employed Daphnet, a publicly accessible benchmark FOG dataset. In terms of effectiveness, the SE-DeepConvNet excels most traditional deep learning models, receiving a score of 95.66% on the accuracy evaluation. © 2021 IEEE.

关键词： Convolutional neural networks

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Machine Vision Inspection of Early Failure and Line-Shaped Defects of Blue Ingan/Gan Light Emitting Diodes Soaked in Liquid Nitrogen for Cryogenic Tests

SSRN

引用

SSRN 2023年

作者： Yang, Chun-Yen Yu, Chih-Yuan Lin, You-Li Hon, Mou-Tuong Yang, Shao-Jui Zhang, Yi-Zong Kang, Hsuan-Chia Kuo, Yaw-Wen Lin, Chia-Feng Wu, You-Lin Chou, Hsin-Hung Chen, Hsiang Li, Yung-Hui Han, Jung Department of Electrical Engineering National Chi Nan University Nantou54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou54561 Taiwan Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan AI Research Center Hon Hai Research Institute Taipei11494 Taiwan Department of Electrical Engineering Yale University New HavenCT06511 United States National Chi Nan University Taiwan

Cryogenic tests were performed on blue InGaN/GaN light emitting diodes (LEDs) by placing the device in liquid nitrogen (LN2). To study the degradation of the LEDs, a combination of optical, electrical, material characterizations were conducted on the LN2 soaked device. Results indicate that noticeable emission spectral shifts were observed during the LED immersion owing the lattice deformation. Moreover, clustering of cracks were generated in the cross–section and line-shaped defects came up on the surface of GaN LED, which was different from the defects on AlInGaP LEDs after LN2 immersion. To track the trend of device damage, computer-assisted opencv-python software were used to zoom in nuanced variations on the certain areas of the emission images. Early degradation of certain areas can be detected by computer-assisted machine vision, which has potential to provide early warning and protective circuits for the system consisting of the initial degraded LEDs. Reliability studies on LN2-soaking InGaN/GaN LEDs show promises to study the reliability issues of GaN device applications in arctic regions and space exploration. © 2023, The Authors. All rights reserved.

关键词： III-V semiconductors

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