检索结果-内蒙古大学图书馆

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

BMC BIOINFORMATICS 2024年第1期25卷 134页

作者： Dolezal, James M. Kochanny, Sara Dyer, Emma Ramesh, Siddhi Srisuwananukorn, Andrew Sacco, Matteo Howard, Frederick M. Li, Anran Mohan, Prajval Pearson, Alexander T. Univ Chicago Dept Med Sect Hematol Oncol Med Ctr Chicago IL 60637 USA Ohio State Univ Dept Internal Med Div Hematol Comprehens Canc Ctr Columbus OH USA Univ Chicago Dept Comp Sci Chicago IL USA

deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

关键词： Digital pathology Computational pathology Software toolkit Whole-slide imaging Explainable AI Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Bubble evolution identification method based on U-Net deep learning

引用

SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Gou, Yuxin Shi, Dongyan Wang, Jiuqiang Zhang, Haifeng Cui, Xiongwei Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Shipbldg Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

This study addresses the challenge of automated analysis of underwater bubble dynamics, as traditional methods for bubble detection often struggle with precision and real-time processing, requiring labour-intensive manual analysis or inefficient image processing techniques. To overcome these limitations, we propose a U-Net deep learning-based method for bubble segmentation and dynamic analysis, utilising high-speed photography (62420 fps) to capture bubble formation and dissipation. The proposed method achieves accurate bubble segmentation through deep feature extraction and upsampling using the U-Net model, with a proportional estimation method applied to calculate bubble size, expansion rate, and movement rate. Validation through manual measurements and CFD simulations confirms the method's reliability. The results demonstrate that the U-Net model enhances the efficiency and accuracy of bubble identification, providing a robust tool for studying underwater bubble dynamics in real-time.

关键词： U-net deep learning bubble evolution identification underwater electric spark experiment expansion and movement rates automated image segmentation

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid Bayesian-optimized CNN-SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis

引用

INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-18页

作者： Arif, Abdul Rao, Ponugoti Gangadhara Prasad, Kalapala JNTUK Univ Coll Engn A Kakinada 533003 India Aditya Univ Dept Mech Engn Surampalem 533437 AP India JNTUK Dept Mech Engn Univ Coll Engn A Kakinada 533003 India

This study presents a novel hybrid Bayesian-optimized CNN-SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis. The hybrid deep learning model, achieves unprecedented accuracy in real-time surface roughness classification and prediction from image data of Inconel 716 superalloy machined surface, eliminating human intervention for feature extraction and selection procedures. The Bayesian optimization algorithm (BOA) was employed to fine-tune the CNN model's hyperparameters. The derived optimal hyperparameters were used to train the CNN model, which achieved a 92.37% classification accuracy on a test dataset. The classification performance was further enhanced by integrating an SVM classifier achieving an accuracy of 98.21%. Additionally, the model's performance was compared with five state-of-the-art deep learning architectures (AlexNet, ResNet50, VGGNet, Faster R-CNN ResNet50, and CNN), with the proposed model showing superior performance in terms of precision (98.02%), sensitivity (98.40%), and F1-score (98.20%). For surface roughness estimation, the hybrid model demonstrated a 96.89% accuracy in predicting roughness values, highlighting its potential for real-time in-process quality monitoring in industrial applications. These results indicate the proposed hybrid approach demonstrated substantially better performance than other popular object detection models. Hence, the presented work demonstrates an efficient end-to-end approach based on the in-process acquired machined surface image analysis by eliminating human intervention in the process of image processing, feature extraction, and feature selection for the real-time classification and prediction of the surface roughness process.

关键词： Surface roughness prediction image-based classification Regression deep learning Convolutional neural networks Hyperparameters Bayesian optimization Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Speed-Up DDPM for real-time Underwater image Enhancement

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3576-3588页

作者： Lu, Siqi Guan, Fengxu Zhang, Hanyu Lai, Haitao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Heilongjiang Peoples R China

Underwater images often suffer from serious color bias and blurred features because of the effect of the water bodies on the light. To enhance underwater images, we present SU-DDPM, a method of real-time underwater image enhancement (UIE) based on a denoising diffusion probabilistic model (DDPM). SU-DDPM outperforms other baseline and generative adversarial network models in underwater image enhancement, thus establishing a new state-of-the-art baseline. SU-DDPM processes images more rapidly than the diffusion model, which makes it competitive with other deep learning-based methods. We demonstrate that if conditional DDPM is used directly for the UIE task, the processing speed is slow, and the enhanced images are of poor quality and show color bias. The quality of the enhanced image is improved by combining the degraded image with the reference image in the diffusion stage to create a fusion-DDPM model. The specificity of the UIE task allows us to accelerate the inference process by changing the initial sampling distribution and reducing the number of iterations in the denoising stage of the model. We evaluate SU-DDPM on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compare it to state-of-the-art models. SU-DDPM ensures increased enhancement quality, and enhancement processing speed is comparable to the speed of real-time enhancement models.

关键词： Underwater image enhancement denoising diffusion probabilistic model (DDPM) underwater image restoration deep learning

来源：评论

学校读者我要写书评

暂无评论

image processing and deep learning Based Road Object Detection System for Safe Transportation

Image Processing and Deep Learning Based Road Object Detecti...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Rizvee Hassan Prito Md. Shafayat Hossain Md. Shajibul Islam Mehzabin Meem Md. Nawab Yousuf Ali Computer Science and Engineering Dept East West University Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface.

关键词： Location awareness Pedestrians Head Roads image processing Object detection real-time systems Safety Object recognition Vehicles

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient real-time visual image adversarial generation and processing algorithm for new energy vehicles

引用

JOURNAL OF real-time image processing 2024年第5期21卷 161页

作者： Li, Yinghuan Liu, Jicheng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

With the rapid development of deep learning in the last decade, generating and processing real-time images have become one of critical methods in intelligent driving systems for new energy vehicles. However, the real-time images captured by sensors are susceptible to variations in various environments, including different weather and lighting conditions. To enhance the real-time image generation performance for new energy vehicles in complex environments, and improve real-time visual image processing capabilities, this study proposes an energy-efficient real-time visual image adversarial generation and processing algorithm, called as ENV-GAN. It hypothesizes a shared latent domain among mixed image domains after analyzing driving situations under various weather and lighting conditions. Mappings are established between different image domains. Besides, a multi-encoder weight-sharing technique is utilized to enhances the generative adversarial network model. Additionally, the algorithm integrates an attention module to enhance the model & acirc;(TM) s image generation. Experimental results and analysis demonstrate that the new algorithm outperforms existing algorithms in tasks such as defogging, rain removal, and lighting enhancement, offering high energy efficiency and low energy consumption.

关键词： New energy vehicles Generative adversarial network image processing Intelligent driving Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of real-time deep learning Methods for image-Based Cancer Diagnostics

引用

JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2024年 17卷 4411-4425页

作者： Sriraman, Harini Badarudeen, Saleena Vats, Saransh Balasubramanian, Prakash Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

deep learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for realtime cancer diagnosis is explored in depth in this work. real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

关键词： artificial intelligence AI machine learning DL CNN healthcare real-time diagnosis classification image processing elastography feedforward neural network

来源：评论

学校读者我要写书评

暂无评论

A real-time traffic sign detection in intelligent transportation system using YOLOv8-based deep learning approach

引用

SIGNAL image AND VIDEO processing 2024年第8-9期18卷 6103-6113页

作者： Tang, Mingdeng Chongqing Vocat Inst Safety & Tech Dept Network & Informat Secur Chongqing 404120 Peoples R China

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD.

关键词： Traffic sign detection deep learning YOLOv8 model real-time Intelligent transportation

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Aware real-time Visual Anomaly Detection With Conformal Prediction in Dynamic Indoor Environments

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4468-4475页

作者： Saboury, Arya Uyguroglu, Mustafa Kemal Eastern Mediterranean Univ Dept Elect & Elect Engn TR-99628 Mersin Turkiye

This letter presents an efficient visual anomaly detection framework designed for safe autonomous navigation in dynamic indoor environments, such as university hallways. The approach employs an unsupervised autoencoder method within deep learning to model regular environmental patterns and detect anomalies as deviations in the embedding space. To enhance reliability and safety, the system integrates a statistical framework, conformal prediction, that provides uncertainty quantification with probabilistic guarantees. The proposed solution has been deployed on a real-time robotic platform, demonstrating efficient performance under resource-constrained conditions. Extensive hyperparameter optimization ensures the model remains dynamic and adaptable to changes, while rigorous evaluations confirm its effectiveness in anomaly detection. By addressing challenges related to real-time processing and hardware limitations, this work advances the state-of-the-art in autonomous anomaly detection. The probabilistic insights offered by this framework strengthen operational safety and pave the way for future developments, such as richer sensor fusion and advanced learning paradigms. This research highlights the potential of uncertainty-aware deep learning to enhance safety monitoring frameworks, thereby enabling the development of more reliable and intelligent autonomous systems for real-world applications.

关键词： Anomaly detection Autoencoders image reconstruction Robots Training Safety Uncertainty Reliability real-time systems Probabilistic logic deep learning for visual perception probability and statistical methods anomaly detection conformal prediction

来源：评论

学校读者我要写书评

暂无评论

Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems

引用

JOURNAL OF MANUFACTURING PROCESSES 2025年 142卷 453-467页

作者： Ramesh, Kaki Deshmukh, Sandip Ray, Tathagata Parimi, Chandu Birla Inst Sci & Technol Pilani Dept Mech Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Comp Sci & Informat Syst Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Civil Engn Hyderabad Campus Hyderabad 500078 Telangana India

Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, image processing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced image processing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This re

关键词： deep learning image processing Assembly Manufacturing industries Quality assurance

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：