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PRIDE: Pediatric Radiological Image Diagnosis Engine Guided by Medical Domain Knowledge

PRIDE: Pediatric Radiological Image Diagnosis Engine Guided ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Yuze Pang, Shijie Wang, Zhuoyao He, Wen Wang, Yingwen Zhao, Rui-Wei Feng, Rui Zhang, Xiaobo Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China Children's Hospital Fudan University National Children's Medical Center China

ISBN: (纸本)9798350386226

Pediatric chest X-rays (CXRs) are crucial for diagnosing respiratory diseases in children. However, most deep learning models perform poorly on pediatric data due to domain gaps, as they are primarily trained on adult datasets with limited pediatric samples. Recent large models, though effective, still struggle with domain-specific terminology and specialized medical reasoning essential for pediatric diagnoses. To address these challenges, we propose PRIDE: a two-stage Pediatric Radiological Image Diagnosis Engine guided by medical knowledge. PRIDE works consistently with real clinical workflows by first gathering medical evidence from patient data, followed by applying clinical knowledge to enhance diagnostic accuracy. In the Multi-source Radiological Findings Recognition Stage, PRIDE integrates insights from generalized medical models and fine-tuned adult and pediatric radiological models to provide comprehensive findings results. In the Knowledge and Evidence-guided Diagnosis Stage, a Multimodal Large Language Model (MLLM) acts as a pediatric clinician, making diagnostic decisions using CXRs, radiological findings, demographic data, and clinical knowledge. Our evaluation on the VinDr-PCXR pediatric dataset demonstrates that PRIDE outperforms existing methods. Ablation studies further confirm the importance and effectiveness of its key components. © 2024 IEEE.

关键词： Pediatrics

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EOFD-Net: Edge Optimization and Feature Denoising for Weakly Supervised Deep Nuclei Segmentation with Point Annotations

EOFD-Net: Edge Optimization and Feature Denoising for Weakly...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xipeng Pan Feihu Hou Zhenbing Liu Siyang Feng Rushi Lan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

Nuclei segmentation is a fundamental and critical step in digital pathological image analysis. Fully supervised nuclei segmentation requires a lot of pixel-by-pixel manual annotation by pathologists, which is very time-consuming and laborious. To minimize the labeling burden of pathologists, this paper uses only point annotations of nuclei data for weakly supervised learning. Specifically, a two-stage model named EOFD-Net with feature denoising and edge optimization is proposed. In the first stage, three weak labels (K-means cluster labels, Voronoi labels, and superpixel labels) with complementary information are used to train the encoder-decoder network to achieve coarse segmentation of nuclei. A feature denoising module(FDM) is designed in the encoder part, which can effectively reduce noise interference. In the second stage, we designed an edge optimization strategy using the prior knowledge of the trained model in the first stage. Confident learning is employed to denoise pseudo-label and rectify the mislabel. These optimized labels are input into the second stage to obtain the final segmentation results. The performance of our method outperforms current state-of-the-art methods on two publicly nuclei segmentation datasets, MoNuSeg and TNBC.

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Large Model-Based Data Augmentation for Imbalanced Text Classification 5

Large Model-Based Data Augmentation for Imbalanced Text Clas...

引用

5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Zhang, Dawei Mi, Rongxin Zhou, Peiyao Jin, Dawei Zhang, Manman Song, Tianhang School of Computer Science Jiangsu University of Science and Technology Jiangsu Zhenjiang China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798350385557

This study focuses on the application of large models to deal with imbalanced data problems in text classification. In view of the central position of text in web data and the negative impact of class imbalance on classifier performance, researchers have explored the method of using large models to generate high-quality minority class samples to enhance model performance. This paper reviews the technical progress of machine learning, deep learning, and large language models and their applications in text classification tasks. Although large models perform well in complex tasks due to their excellent language understanding ability, traditional machine learning and deep learning methods are popular in text classification scenarios that require fast response due to their simple structure and higher computational efficiency. This study proposes a data augmentation technique inspired by SMOTE, which uses a large language model combined with a simple prompt engineering strategy to generate high-quality minority samples. The experimental results show that the proposed method significantly improves the macro average precision, recall and F1 score on multiple text classification models, and effectively alleviates the challenge of class imbalance. © 2024 IEEE.

关键词： Computational efficiency

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Application of intelligent Schedule Management in Volunteer Service System

Application of Intelligent Schedule Management in Volunteer ...

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Data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Yongxin Li Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

As the demand for volunteer services grows, enhancing the efficiency and management of these services has become a key issue. This paper explores methods of integrating intelligent scheduling management functions with volunteer service systems to optimize volunteer time management and task allocation. By analyzing existing volunteer service systems, we propose a novel system framework that integrates intelligent scheduling management. This framework updates volunteer schedules in real- time, automatically matches tasks with volunteers' time windows, and optimizes task distribution using smart algorithms. Experimental results show that volunteer service systems with integrated intelligent scheduling management significantly improve task allocation accuracy and volunteer satisfaction. This research provides new ideas and practical basis for the intelligent development of volunteer services.

关键词： Training Performance evaluation Schedules Data analysis Organizations Scheduling User experience Resource management System analysis and design Testing

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Research on YOLOv8-Based Household Item Detection for Indoor Scenes

Research on YOLOv8-Based Household Item Detection for Indoor...

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Data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Jingwen Wu Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

This research is driven by the growing need for efficient, scalable object detection in smart home applications with real-time performance and resource constraints. In this study, we utilize the YOLOv8 deep learning model for household item detection, focusing on its performance in real-time scenarios. We compare different versions of YOLOv8—specifically, YOLOv8n, YOLOv8s, and YOLOv8m—using a preprocessed custom dataset. The results indicate that YOLOv8n stands out due to its lower parameter count and faster inference speed, making it ideal for real-time applications, even though it has slightly lower precision and recall. In contrast, while YOLOv8s and YOLOv8m show improvements in certain metrics, their higher computational demands limit their effectiveness in resource-constrained settings. Overall, YOLOv8n effectively balances speed and accuracy, offering a viable solution for household item detection.

关键词： Training Measurement Analytical models Accuracy Computational modeling Heuristic algorithms Smart homes Object detection Real-time systems Tuning

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CLIP-based Text-to-image Pedestrian Retrieval Algorithm

CLIP-based Text-to-image Pedestrian Retrieval Algorithm

引用

Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xubin Shi Baohua Qiang Ruidong Chen Yuan Xie Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Text-to-image pedestrian retrieval involves querying a database of images based on a given textual description to find matching images. The main challenge lies in learning how to map visual and textual patterns to a common latent space. This paper improves upon the implicit learning inference framework based on the CLIP model, aiming to learn the relationship between images and text and enhance overall text-image matching. Through text matching learning, fine-grained detail representation learning is promoted. First, a masked language modeling paradigm is designed within the implicit relationship inference module. Then, cross-modal interaction is achieved through the modality alignment module using decomposable attention. Feed aligned image and text features into a multimodal synergy encoder to achieve cross modal interaction, and train the model using distribution consistency loss and ID loss. The improved method achieves better results on the RSTPReid public dataset, with the Rank-1 accuracy improving by approximately 3% compared to existing methods.

关键词： Representation learning Visualization Computer vision Pedestrians Databases Image processing Semantics Text to image Transformers Feeds

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Facial Recognition-Based Classroom Attendance System Utilizing Deep Learning

Facial Recognition-Based Classroom Attendance System Utilizi...

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Data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Cong Zhou Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

In recent years, artificial intelligence technologies, especially facial recognition, have made significant strides. With its high precision and efficiency, facial recognition has shown great potential and advantages in areas such as identity verification and behavior analysis. Classroom learning is the main channel through which students acquire knowledge, develop skills, and improve their competencies. A well-organized classroom attendance system is essential for ensuring the effective implementation and smooth execution of teaching plans. This system is based on an OpenCV and PyQt5 facial recognition client, utilizing the LBPH algorithm to achieve efficient facial recognition. In addition to providing a simple check-in function, the system offers a complete registration process and data management features. In practical use, students simply need to check in by facing the camera, allowing the system to automatically recognize the student's face and record the attendance data. This significantly simplifies the check-in process for both teachers and students, thereby improving classroom management efficiency.

关键词： Deep learning Accuracy Databases Face recognition Education Learning (artificial intelligence) Cameras Testing

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

引用

4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

This paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. The method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. The proposed method starts with data collection of the construction site environment. Then produce accurate bounding boxes. These bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. The proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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Design of Smart Home System Based on ZigBee technology

Design of Smart Home System Based on ZigBee Technology

引用

Data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Yinfan Liang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

With the rise of the Internet of Things (loT) and digital homes, smart home technology has also advanced. Smart homes can proactively sense people's needs, providing users with a more comfortable and secure living environment, while also saving energy and enhancing safety, thereby greatly improving the quality of life. Thanks to loT technology, smart home devices can connect to the home network, enabling data sharing and centralized control. This project applies ZigBee and MQTT protocols to the field of smart homes, achieving intelligent control and remote control of devices such as lighting, appliances, and security. The ZigBee protocol is used for networking, connecting terminal nodes in the facility layer with the coordinator node, while the MQTT protocol is used to connect the coordinator node to the internet. This project has realized remote control, intelligent control, and smart security of the home system, providing users with a more convenient and comfortable home experience.

关键词： Home appliances Protocols Zigbee Lighting Smart homes Safety Security Internet of Things Remote control intelligent control

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DIR-YOLOv5: A real-time Drone-perspective Infrared object detection method based on YOLOv5

DIR-YOLOv5: A real-time Drone-perspective Infrared object de...

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2023 Applied Optics and Photonics China: Infrared Devices and Infrared technology;and Terahertz technology and Applications, AOPC 2023

作者： Yuexing, Wang Chuangang, Xu Jianzhong, Su Jinwen, Tian National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Tianjin Jinhang Institute of Technical Physics Tianjin300308 China

ISBN: (纸本)9781510672260

With the advancement of drone technology, object detection from the perspective of drones has found extensive applications in various fields, including surveillance, search operations, and reconnaissance tasks. Currently, most drones in the market are equipped with visible light imagers, while some high-end drones are equipped with infrared imaging detectors capable of performing infrared object detection tasks. Infrared imaging utilizes a passive imaging mode, enabling it to detect thermal radiation emitted by objects. As a result, it offers the distinct advantage of continuous operation without being restricted by daylight conditions. In comparison to visible imaging, infrared imaging uses longer wavelengths and possesses a certain level of penetration capability through clouds and smoke. Consequently, infrared object detection represents a significant research area within the field of object detection. However, detecting infrared objects, especially small ones, remains challenging due to the complexity of background information, lower resolution compared to visible images, and the lack of shape and texture information in infrared images. In response to these challenges, this study proposes a real-time drone-perspective infrared (IR) object detection method based on the YOLOv5 framework, known as DIR-YOLOv5. To effectively address the challenge of infrared vehicles occupying fewer pixels in the drone's perspective image and making objects difficult to detect, the coordinate attention (CA) for feature enhancement is introduced. we also introduce a Spatial-Channel dynamic and query-aware sparse attention mechanism (SCBiFormer), which is optimized based on BiFormer. Additionally, we redefine the loss function as the Repulsion Loss function to tackle the problem of infrared vehicle objects gathering and overlapping occlusion in scenarios like parking lots. Furthermore, we expand the ISVD infrared image object detection dataset to include multiple scenarios and conduct

关键词： Image enhancement

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