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7th China High-resolution Earth Observation Conference, CHREOC 2020

作者： Sun, Zengguo Zhao, Guodong Chen, Weirong Damaševičius, Robertas Woźniak, Marcin Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an China China Centre for Resources Satellite Data and Application Beijing China Department of Applied Informatics Vytautas Magnus University Kaunas Lithuania Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (纸本)9789811657344

Synthetic aperture radar (SAR) has the characteristics of all-weather imaging and is widely used in various fields. However, owe to coherent imaging mechanism, speckle appears in SAR images, which seriously affects the interpretation of images. Due to merits of the strong directional sensitivity and the optimal sparsity, shearlet is used to construct an edge detector for SAR images. The detector firstly converts the constructed even symmetric shearlet into odd symmetry by Hilbert transform, then determines the main direction of the edge according to the odd symmetric shearlet coefficient of the SAR image, and calculates the possibility of edge existence according to both odd and even symmetric shearlet coefficient. Experiments show that, in comparison with the traditional difference-based edge detectors, the proposed edge detector has stronger ability of anti-speckle interference, and can better detect the edge information in SAR images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speckle

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Instance Segmentation of Dense and Overlapping Objects via Layering

arXiv

引用

arXiv 2022年

作者： Chen, Long Wu, Yuli Merhof, Dorit Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Instance segmentation aims to delineate each individual object of interest in an image. State-of-the-art approaches achieve this goal by either partitioning semantic segmentations or refining coarse representations of detected objects. In this work, we propose a novel approach to solve the problem via object layering, i.e. by distributing crowded, even overlapping objects into different layers. By grouping spatially separated objects in the same layer, instances can be effortlessly isolated by extracting connected components in each layer. In comparison to previous methods, our approach is not affected by complex object shapes or object overlaps. With minimal post-processing, our method yields very competitive results on a diverse line of datasets: C. elegans (BBBC), Overlapping Cervical Cells (OCC) and cultured neuroblastoma cells (CCDB). The source code is publicly available 1 © 2022, CC BY.

关键词： Semantic Segmentation

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Collection: Wound Healing Assay dataset (WHAD) and Cell Adhesion and Motility Assay dataset (CAMAD)

IEEE Data Descriptions

引用

IEEE data Descriptions 2024年 1卷 95-102页

作者： Leonardo O. Iheme Sevgi Onal Yusuf S. Erdem Mahmut Ucar Ozden Yalcin-Ozuysal Devrim Pesen-Okvur Behcet U. Toreyin Devrim Unay Epam Systems Istanbul Türkiye Department of Biotechnology and Bioengineering Izmir Institute of Technology Graduate School of Science and Engineering Izmir Türkiye Center for Advanced Biomaterials for Health Care Istituto Italiano di Tecnologia Napoli Italy Department of Electrical and Electronics Engineering Faculty of Engineering Izmir Democracy University Izmir Türkiye Department of Molecular Biology and Genetics Faculty of Science Izmir Institute of Technology Izmir Türkiye Department of Artificial Intelligence and Data Engineering Istanbul Technical University Informatics Institute Istanbul Türkiye

This article introduces two novel datasets for cell motility and wound healing research: the wound healing assay dataset (WHAD) and the cell adhesion and motility assay dataset (CAMAD). WHAD comprises time-lapse phase-contrast images of wound healing assays using genetically modified MCF10A and MCF7 cells, while CAMAD includes MDA-MB-231 and RAW 264.7 cells cultured on various substrates. These datasets offer diverse experimental conditions, comprehensive annotations, and high-quality imaging data, addressing gaps in existing resources. The collection methods, experimental designs, and annotation processes are detailed, emphasizing the reliability and utility of our datasets. WHAD and CAMAD enable researchers to develop robust algorithms for cell tracking, segmentation, and behavior analysis in phase-contrast microscopy images. The applications of these datasets span cancer research, image processing technique development, and automated analysis of wound healing and cell motility assays. By providing these resources, we aim to accelerate progress in biomedical image analysis and cell biology research. IEEE SOCIETY/COUNCIL Engineering in Medicine and Biology Society (EMBS) data TYPE/LOCATION Images; Izmir, Türkiye data DOI/PID 10.21227/f77j-j355

关键词： Wounds Microscopy Substrates Image segmentation Phase change materials Annotations Breast cancer Biomedical imaging Adhesives Pipelines

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Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection

arXiv

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arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Azad, Babak Aghdam, Ehsan Khodapanah Velichko, Yury Bagci, Ulas Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany School of Electrical Engineering Iran University of Science and Technology Iran South Dakota State University Brookings United States Department of Electrical Engineering Shahid Beheshti University Iran Department of Radiology Northwestern University Chicago United States Faculty of Informatics and Data Science University of Regensburg Germany

Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87% and +0.76% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub. © 2023, CC BY.

关键词： Textures

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BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

引用

IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Hu, Kaimiao He, Yuan Wei, Jianguo Sun, Changming Geng, Jie Wei, Leyi Su, Ran Tianjin University College of Intelligence and Computing Tianjin30000 China CSIRO Data61 SydneyNSW1710 Australia Tianjin University Tianjin Key Laboratory of Cardiovascular Emergency and Critical Care Tianjin Municipal Science and Technology Bureau Department of Cardiology Chest Hospital Tianjin China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen China

Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations. ©2013 IEEE.

关键词： Contrastive Learning

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

Attention Swin U-Net: Cross-Contextual Attention Mechanism f...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ehsan Khodapanah Aghdam Reza Azad Maral Zarvani Dorit Merhof Department of Electrical Engineering Shahid Beheshti University Tehran Iran Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Computer Engineering Alzahra University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Bayern Germany

Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range dependency, which is crucial for the segmentation task in medical images. To address this limitation, recently a Transformer based U-Net architecture that replaces the CNN blocks with the Swin Transformer module has been proposed to capture both local and global representation. In this paper, we propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. In our design, we seek to enhance the feature re-usability of the network by carefully designing the skip connection path. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism. By performing a comprehensive ablation study on several skin lesion segmentation datasets, we demonstrate the effectiveness of our proposed attention mechanism.

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A Curious New Result of Resolution Strategies in Negation-Limited Inverters Problem

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2022年第4期49卷 1212页

作者： Ando, Ruo Takefuji, Yoshiyasu National Institute of Informatics 2-1-2 HitotsubashiTokyo Nasrudeen city101-8430 Japan Faculty of Data Science Musashino University 3-3-3 AriakeTokyo Koto-Ku135-8181 Japan

Generally, the negation-limited inverters problem is a puzzle of constructing an inverter with AND gates and OR gates, and a few inverters. This paper introduces a curious new result about the effectiveness of two powerful ATP (Automated Theorem Proving) strategies in tackling negation-limited inverter problems. Two resolution strategies are UR (Unit Resulting) resolution and Hyper-Resolution. In the experiment, we cope with two kinds of automated circuit construction: three input/output inverters and four input/output BCD Counter Circuit. Both circuits are constructed with a few limited inverters. Curiously, it has been turned out that UR resolution is drastically faster than Hyper-Resolution in the measurement of the size of SoS (Set of Support). Besides, we discuss the syntactic and semantic criteria, which might cause a considerable difference in computation cost between UR resolution and Hyper-Resolution. © 2022,IAENG International Journal of Computer science. All Rights Reserved.

关键词： Semantics

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Opportunities and Challenges Surrounding the Use of Wearable Sensor Bracelets for Infectious Disease Detection During Hajj: Qualitative Interview Study

引用

JMIR Formative Research 2025年 9卷 e60484页

作者： Maddah, Noha Verma, Arpana Ainsworth, John Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre Centre for Health Informatics The University of Manchester Manchester United Kingdom Department of Health Services and Hospitals Administration Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia Division of Population Health Health Services Research & Primary Care Faculty of Biology Medicine and Health Manchester Academic Health Sciences Centre The University of Manchester Manchester United Kingdom

Background: Wearable sensor bracelets have gained interest for their ability to detect symptomatic and presymptomatic infections through alterations in physiological indicators. Nevertheless, the use of these devices for public health surveillance among attendees of large-scale events such as hajj, the Islamic religious mass gathering held in Saudi Arabia, is currently in a nascent phase. Objective: This study aimed to explore hajj stakeholders’ perspectives on the use of wearable sensor bracelets for disease detection. Methods: We conducted a qualitative, theoretically informed, interview-based study from March 2022 to October 2023 involving a diverse sample of hajj stakeholders, including technology experts, health care providers, and hajj service providers. The study was guided by the task-technology fit model and the unified theory of acceptance and use of technology to provide a comprehensive understanding of the factors influencing the acceptance and use of the technology. Semistructured in-depth interviews were used to capture perspectives on using wearable sensor bracelets for infectious disease detection during hajj. Thematic analysis of interview transcripts was conducted. Results: A total of 14 individuals were interviewed. In total, 4 main themes and 13 subthemes emerged from the study, highlighting crucial challenges, considerations, recommendations, and opportunities in the use of wearable sensor bracelets for the presymptomatic detection of infectious diseases during hajj. Implementing wearable sensor bracelets for disease detection during hajj faces obstacles from multiple perspectives, encompassing users, implementing stakeholders, and technological factors. Hajj stakeholders were concerned about the substantial financial and operational barriers. The motivation of implementing stakeholders and users is essential for the acceptance and uptake of devices during hajj. Successful integration of wearables into the hajj surveillance system depends on sev

关键词： artificial intelligence hajj infectious diseases presymptomatic detection task-technology fit unified theory of acceptance and use of technology wearable sensor

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Blockchain-based data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

引用

Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Azman Azmi Farashazillah Yahya Ervin Gubin Moung Hasimi Sallehudin Rio Guntur Utomo Nur Afrina Azman Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Data Technologies and Application Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia School of Computing Telkom University Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Iskandar Puteri Johor Malaysia

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem.

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Characterizing Continual Learning Scenarios and Strategies for Audio Analysis

arXiv

引用

arXiv 2024年

作者： Bhatt, Ruchi Kumari, Pratibha Mahapatra, Dwarikanath Saddik, Abdulmotaleb El Saini, Mukesh The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India The Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Inception Institute of AI Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Canada The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India Abu Dhabi United Arab Emirates

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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