With the rapid development of digital technology and deep learning, recovering 3D scene information and reconstructing human bodies from a single image has become a focal point of research in computer vision and compu...
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In the medical field, image segmentation is critical for disease diagnosis and treatment. To address challenges such as data heterogeneity and privacy concerns, this paper proposes a federated learning-based DUnet app...
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Reconstructing high-quality images at low measurement rates remains a problem in single-pixel imaging. Traditional approaches, including methods based on compressed sensing and end-to-end deep learning models, often f...
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The proceedings contain 21 papers. The special focus in this conference is on Medical Optical Imaging and Virtual Microscopy image Analysis. The topics include: Deep Learning for Classifying Anti-Shigella Opsono-...
ISBN:
(纸本)9783031777851
The proceedings contain 21 papers. The special focus in this conference is on Medical Optical Imaging and Virtual Microscopy image Analysis. The topics include: Deep Learning for Classifying Anti-Shigella Opsono- Phagocytosis-Promoting Monoclonal Antibodies;multi-target Stain Normalization for Histology Slides;intensity Inhomogeneity Correction for Large Panoramic Electron Microscopy images;Fully Automated CTC Detection, Segmentation and Classification for Multi-channel IF Imaging;lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides;TRP-Net: Transformer with RMM and PPM for High-Efficiency Circulating Abnormal Cells Detection in Multichannel Fluorescence Imaging;color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals;Learned image Compression for HE-Stained Histopathological images via Stain Deconvolution;CLSMI2T3: 3D CLSM Vasculature Volume reconstructionfrom A Single 2D Slice by Off-Focal Plane Signal Using Synthetic data;Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging;MDSN: Multi-stage Context-Aware Nuclei Detection-Segmentation Network;structured Model Pruning for Efficient Inference in Computational Pathology;Histopathology image Embedding Based on Foundation Models Features Aggregation for DLBCL Patient Treatment Response Prediction;EM-Compressor: Electron Microscopy image Compression in Connectomics with Variational Autoencoders;AC-UNet: A Self-adaptive Cropping Approach for Kidney Pathology image Segmentation;SAM-Glomeruli: Enhanced Segment Anything Model for Precise Glomeruli Segmentation;A Robust Deep Learning Method for WSI-Level Diseased Glomeruli Segmentation;ensembled SegNeXt Based Glomeruli Segmentation;glomeruli Segmentation in Whole-Slide images: Is Better Local Performance Always Better?.
In the field of semi-supervised medical image segmentation, we often face three main challenges. Firstly, there’s an issue with the empirical distribution not quite matching between labeled and unlabeled images. If w...
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In recent years, the application scenarios of satellite remote sensing images have become increasingly widespread. However, due to limited acquisition equipment and cost constraints, the images obtained by satellite s...
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The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residu...
ISBN:
(纸本)9781643685762
The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residual learning for fruits of ceremonial plants recognition;PiVisionSort: integrating image processing and machine learning for material recognition on conveyor belts;intelligent detection of potholes using SSD algorithm and auto-alert notification system for user;the use of motion and gaze features to detect speaking intention in VR-mediated communication environments;optimized retinal vessel segmentation using IS-Net and high-resolution dataset;a framework for adopting machine learning in the clinical domain;and a study on a domain BERT-based named entity recognition method for faulty text.
Massive data is continuously generated from a variety of real-world scenarios and fields in all walks of life, including data recorded in hardware sensors, huge amounts of text, voice andimagedata, various user data...
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The proceedings contain 46 papers. The special focus in this conference is on Security and Information Technologies with AI. The topics include: Authentication of image Fidelity in Watermarking-QR-Code Approving Copyr...
ISBN:
(纸本)9789819777853
The proceedings contain 46 papers. The special focus in this conference is on Security and Information Technologies with AI. The topics include: Authentication of image Fidelity in Watermarking-QR-Code Approving Copyright Systems;a Zero-Watermarking image Scheme in Normalized Cross-Correlation with Robust Copyright Protections;a Novel Approach for Detecting and Analyzing Cyber-Attacks in Cyber-Physical Systems;white-Box Penetration Testing for Hash Collision Attack on Web Applications;towards Zero Trust for Financial Sectors: A Proposed Framework on Trust Evaluation;FiT-DPKI: Decentralized Public Key Infrastructure with Flexibility and Transparency for IoT Networks;Revolutionizing Healthcare: Case Studies of AI Algorithms Transforming the Field of Medicine;analysis and Detection of Abnormal Transactions on Ethereum;feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models;Securing NLP Systems: A Comprehensive AI-Based Approach;A New Scheme Modeling Gym Membership Transactions with NFT Systems;A Text to Human-Like Speech Using Tacotron-Based TTS Model;A Novel Malware Classification Using CNN-SVM Deep Learning Method Based on Transfer Learning Architecture;enhancing Project Programming Hour Prediction with Regression Analysis Techniques—A Case Study of Company D;a New Traceable One-Time Address Scheme Secure Against Privilege Escalation Attack;An Object-Based Multi-level Authentication Framework for AR e-Book;a Door Lock System Based on Visual Cryptography;Automatic APT Attack reconstruction Supporting Lateral Movement;using Open-Source Intelligence to Archive Criminal Organizations;FIDO-Based Access Control Mechanism in Named data Networking;empirical Approach to a Fine-Tuning Using Forgetting in Large Language Models;automatic Wound Segmentation with Deep Convolutional Neural Networks;Prediction of the Prevalence of COVID-19 Using Epidemic Differential Equations and Deep Learning Network.
Potatoes are one of the most cultivated and consumed crops worldwide. Disease control in potato plants is crucial for efficient production of potatoes. In this paper, we propose a method to detect and classify disease...
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ISBN:
(纸本)9783031744396;9783031744402
Potatoes are one of the most cultivated and consumed crops worldwide. Disease control in potato plants is crucial for efficient production of potatoes. In this paper, we propose a method to detect and classify diseases from the images of potato leaves. For this, we propose a deep neural architecture to classify the plant diseases from leaf images. We have used the PSOK image segmentation method to segment the leaf images into meaningful features. data augmentation techniques like flipping, rotating, skewing, and zooming are used to increase the dataset. We compare the performance of our proposed model with pretrained model. We show that our proposed architecture combined with XGBoost trained on the augmented dataset achieves an accuracy of 97.81%, outperforming the existing methods.
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