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xU-NetFullSharp: The Novel Deep Learning Architecture for Chest X-ray Bone Shadow Suppression

BIOMEDICAL signal processing AND CONTROL 2025年 100卷

作者： Schiller, Vojtech Burget, Radim Genzor, Samuel Mizera, Jan Mezina, Anzhelika Brno Univ Technol Dept Telecommun FEEC Technicka 12 Brno 61600 Czech Republic Univ Hosp Olomouc Dept Resp Dis & TB Zdravotniku 248-7 Olomouc 77900 Czech Republic Palacky Univ Fac Med & Dent Zdravotniku 248-7 Olomouc 77900 Czech Republic

Background and objectives: Chest X-ray image (CXR) is vital for screening, preventing, and monitoring various lung diseases. In particular, the early detection of lung cancer can significantly improve patients' chances of survival and quality of life. Unfortunately, approximately 82-95 % of missed pulmonary nodules are estimated to be obscured by rib shadows, making them difficult to recognize. This study addresses this problem by considering the rib shadows in CXRs as noise that can be reduced using deep learning. The result of the proposed model is a CXR with improved clarity for easier and more accurate analysis by radiologists or computer algorithms. methods: An automated deep learning-based model for bone shadow suppression from frontal CXRs, called xUNetFullSharp, was proposed. This network is inspired by the most modern U-NetSharp architecture and was modified using different approaches to preserve as many details as possible and accurately suppress bone shadows. For comparison, recent state-of-the-art models were implemented and trained. JSRT, VinDr-CXR, and Gusarev DES datasets were utilized for the experiments, where the JSRT dataset was extensively augmented. Results: The performance of the proposed xU-NetFullSharp was analyzed using statistical measures and compared with that of other architectures. The proposed model significantly outperformed the others, reaching the best values of the most used metrics (0.9846 SSIM;0.9870 MS-SSIM). It also achieves a correlation of 96.31 % and an intersection of 10.0285 between the predicted and ground truth histograms, together with the smallest value of the Bhattacharyya distance. The obtained results were validated by experts from the University Hospital Olomouc with positive feedback, thus achieving the best objective and subjective results. The proposed method has the potential to be implemented in hospital environments. Conclusion: A comprehensive comparison of the proposed architecture with state-of-the-art met

关键词： Deep learning Bone shadow suppression X-ray images Medical Imaging neural networks Convolutional neural networks U -Net image denoising

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Cuboid-Net: A multi-branch convolutional neural network for joint space-time video super resolution

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IET image processing 2023年第14期17卷 4089-4101页

作者： Fu, Congrui Yuan, Hui Xu, Hongji Zhang, Hao Shen, Liquan Shandong Univ Sch Informat Sci & Engn Qingdao Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

The demand for high-resolution videos has been consistently rising across various domains, propelled by continuous advancements in societal. Nonetheless, limitations in imaging and economic factors often result in obtaining low-resolution images. The currently available space-time video super-resolution methods often fail to fully exploit the information existing within the spatio-temporal domain. To address this problem, the issue is tackled by conceptualizing the input low-resolution video as a cuboid structure. An innovative methodology called "Cuboid-Net", which incorporates a multi-branch convolutional neural network, is introduced. Cuboid-Net is designed to collectively enhance the spatial and temporal resolutions of videos, enabling the extraction of rich and meaningful information across both spatial and temporal dimensions. Specifically, the input video is taken as a cuboid to generate different directional slices as input for different branches of the network. The proposed network contains four modules, that is, a multi-branch-based hybrid feature extraction module, a multi-branch-based reconstruction module, a first-stage quality enhancement module, and a second-stage cross frame quality enhancement module for interpolated frames only. Experimental results demonstrate that the proposed method is not only effective for spatial and temporal super-resolution of video but also for spatial and angular super-resolution of light field. A multi-branch convolutional neural network (Cuboid-Net) for joint space-time video super resolution is proposed. Specifically, the input video is taken as a cuboid to generate different directional slices as input for different branches of the ***

关键词： image enhancement image resolution video signal processing

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SE-NDEND: A novel symmetric watermarking framework with neural network-based chaotic encryption for Internet of Medical Things

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BIOMEDICAL signal processing AND CONTROL 2024年 90卷

作者： Chai, Xiuli Tang, Zongwei Gan, Zhihua Lu, Yang Wang, Binjie Zhang, Yushu Henan Univ Huaihe Hosp Dept Cardiovasc Med Kaifeng 475000 Peoples R China Henan Univ Med Imaging Res Inst Kaifeng 475000 Peoples R China Henan Univ Henan Engn Res Ctr Ind Internet Things Sch Artificial Intelligence Zhengzhou 450046 Peoples R China Henan Univ Inst Intelligent Network Syst Intelligent Data Proc Engn Res Ctr Henan Prov Sch Software Kaifeng 475004 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

The development of the Internet of Medical Things heavily relies on big data, and data security based on medical images has become a growing concern in society. Digital watermarking serves as a crucial technique for protecting and tracing medical image data copyright, as well as enabling forensic analysis. However, existing deep watermarking methods often neglect the protection of watermarks after extraction, leading to potential copyright disputes. To address this issue, this paper proposes SE-NDEND, a novel symmetric watermarking framework with neural network-based chaotic encryption for the Internet of Medical Things that significantly enhances the effectiveness and security of watermarking while maintaining robustness. Specifically, the SE-NDEND leverages neural networks to simulate chaotic systems and generate chaotic sequences, mitigating the complexity and high cost of implementing chaotic systems using hardware circuits. Moreover, we introduce a new noise layer with Moire ' distortion that interacts with the decoder, forming a symmetric network structure that bolsters the robustness of watermarking. Parameters are jointly trained and shared during the training process to counteract potential interference from the noise layer. Experimental results validate the effectiveness of SE-NDEND in enhancing copyright protection, traceability, and forensic capabilities, surpassing existing deep learning methods in terms of visual quality (with PSNR of 45.8492 dB and SSIM of 0.9874), security, and robustness. The proposed framework can find application in protecting medical image data in the Internet of Medical Things.

关键词： Medical image data Watermarking Effectiveness Moire distortion Copyright protection

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SSHNN: SEMI-SUPERVISED HYBRID NAS NETWORK FOR ECHOCARDIOGRAPHIC image SEGMENTATION 49

SSHNN: SEMI-SUPERVISED HYBRID NAS NETWORK FOR ECHOCARDIOGRAP...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chen, Renqi Luo, Jingjing Nian, Fan Cen, Yuhui Peng, Yiheng Yu, Zekuan Fudan Univ Inst AI&Robot Acad Engn Shanghai Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a "strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.

关键词： Medical image segmentation Hybrid NAS Semi-supervised learning Transformer

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Deep learning based multiclass classification for citrus anomaly detection in agriculture

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signal image AND VIDEO processing 2024年第11期18卷 8077-8088页

作者： Ergun, Ebru Recep Tayyip Erdogan Univ Fac Engn & Architecture Dept Elect Elect Engn Rize Turkiye

In regions where citrus crops are threatened by diseases caused by fungi, bacteria, pests and viruses, growers are actively seeking automated technologies that can accurately detect citrus anomalies to minimize economic losses. Recent advances in deep learning techniques have shown potential in automating and improving the accuracy of citrus anomaly categorization. This research explores the use of deep learning methods, specifically DenseNet, to construct robust models capable of accurately distinguishing between different types of citrus anomalies. The dataset consists of high-resolution images of different orange leaves of the species Citrus sinensis osbeck, collected from orange groves in the states of Tamaulipas and San Luis Potosi in northeastern Mexico was used in study. Experimental results demonstrated the effectiveness of the proposed deep learning models in simultaneously identifying 12 different classes of citrus anomalies. Evaluation metrics, including accuracy, recall, precision and the confusion matrix, underscore the discriminative power of the models. Among the convolutional neural network architectures used, DenseNet achieved the highest classification accuracy at 99.50%. The study concluded by highlighting the potential for scalable and effective citrus anomaly classification and management using deep learning-based systems.

关键词： Citrus anomaly classification Agricultural management Convolutional neural networks Deep learning Multi-class classification

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A novel hybrid machine learning approach for traffic sign detection using CNN-GRNN

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1283-1303页

作者： Pandurangan, Raji Jayaseelan, Samuel Manoharan Rajalingam, Suresh Angelo, Kandavalli Michael Saveetha Engn Coll Dept Elect & Commun Engn Thandalam Tamil Nadu India Sir Isaac Newton Coll Engn & Technol Dept Elect & Commun Engn Nagapattinam Tamil Nadu India Kings Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India DVR & Dr HS MIC Coll Technol Dept Elect & Commun Engn Kanchikacherla India

The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)-General Regression neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system.

关键词： Traffic signal images traffic signs median filter gabor filter forecasting

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CAFCT-NET: A CNN-TRANSFORMER HYBRID NETWORK WITH CONTEXTUAL AND ATTENTIONAL FEATURE FUSION FOR LIVER TUMOR SEGMENTATION 31

CAFCT-NET: A CNN-TRANSFORMER HYBRID NETWORK WITH CONTEXTUAL ...

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2024 International Conference on image processing

作者： Kang, Ming Ting, Chee-Ming Ting, Fung Fung Phan, Raphael C. -W. Monash Univ Sch Informat Technol Subang Jaya Malaysia

ISBN: (纸本)9798350349405;9798350349399

Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans. In this paper, we propose a Contextual and Attentional feature Fusions enhanced Convolutional neural Network (CNN) and Transformer hybrid network (CAFCT-Net) for liver tumor segmentation. We incorporate three novel modules in the CAFCT-Net architecture: Attentional Feature Fusion (AFF), Atrous Spatial Pyramid Pooling (ASPP) of DeepLabv3, and Attention Gates (AGs) to improve contextual information related to tumor boundaries for accurate segmentation. Experimental results show that the proposed model achieves a mean Intersection over Union (IoU) of 76.54% and Dice coefficient of 84.29%, respectively, on the Liver Tumor Segmentation Benchmark (LiTS) dataset, outperforming pure CNN or Transformer methods, e.g., Attention U-Net and PVTFormer.

关键词： Medical image semantic segmentation lesion segmentation computed tomography (CT) attention mechanism CNN-transformer fusion

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Lightweight lane line detection based on learnable cluster segmentation with self-attention mechanism

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第3期17卷 518-529页

作者： Yang, Qin Ma, Yahong Li, Linsen Su, Chang Gao, Yujie Tao, Jiaxin Huang, Zhentao Jiang, Rui Xijing Univ Sch Informat Engn Xian Peoples R China Autocore Ai Nanjing Peoples R China

Pixel segmentation is one of the most commonly used deep learning methods for modern lane line detection. Although deep segmentation outperforms traditional methods, there are two main problems: slow speed and limited receptive field. In response to these problems, this paper proposes a lightweight lane line detection algorithm based on learnable cluster segmentation and self-attention mechanism, which has extremely fast speed and the ability to adapt to real scenes. The lane detection process is considered as clustering under row segmentation. The data is processed through row segmentation and fed into a self-attention mechanism. In addition to the benchmark dataset for lane detection, the algorithm was ported to real vehicles for real-time road testing. Two tests show that our method performs very well on TuSimple, with an accuracy of 97.15%, an F1 score of 73.5 on CULane, and a speed of 142.7 frames per second (FPS), which solves the problem of slow cluster segmentation, while improving the accuracy of row segmentation. In the new scenario, the method has a misjudgment rate of only 6.7% for lane line points, which is suitable for the high standard requirements of autonomous driving.

关键词： traffic engineering computing image segmentation feature extraction slow cluster segmentation self-attention mechanism pattern clustering row segmentation extremely fast speed learning (artificial intelligence) lane line points lane detection process modern lane line detection neural nets pixel segmentation Optical, image and video signal processing Computer vision and image processing techniques commonly used deep learning methods deep segmentation convolutional neural nets limited receptive field Traffic engineering computing object detection lightweight lane line detection algorithm deep learning (artificial intelligence) image recognition slow speed road vehicles learnable cluster segmentation

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Lightweight defocus deblurring network for curved-tunnel line scanning using wide-angle lenses

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UNDERGROUND SPACE 2025年第1期20卷 218-240页

作者： Qin, Shaojie Qi, Taiyue Huang, Xiaodong Liang, Xiao Minist Educ Key Lab Transportat Tunnel Engn Chengdu 610036 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610036 Peoples R China Guangzhou Metro Construct Management Co Ltd Guangzhou 510000 Peoples R China Shandong Energy Grp Co Ltd Jinan 250000 Peoples R China

High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection. However, due to the curvature of the tunnel, there are variations in object distance that exceed the depth of field of the lens, resulting in uneven defocus blur in the captured images. This can significantly affect the accuracy of defect recognition. While existing deblurring algorithms can improve image quality, they often prioritize results over inference time, which is not ideal for high-speed tunnel image acquisition. To address this issue, we developed a lightweight tunnel structure defect deblurring network (TSDDNet) for curved-tunnel line scanning with wide-angle lenses. Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time. The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). A raw dataset containing tunnel blur images was created using a highresolution line scan camera and used to train and test our model. TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888, while using one-third of the parameters of comparable alternatives. Moreover, our method exhibited a higher computational speed than that of conventional methods, with inference times of 8.82 ms for a single 512 x 512 pixels image patch and 227.22 ms for completely processing a 2048 x 2560 pixels image. The test results indicated that the structural scalability of the network allows it to accommodate large inputs, making it effective for high-resolution images.

关键词： image deblurring Tunnel defect detection Defocus deblurring Convolutional neural networks Massive image acquisition

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Weakly supervised glottis segmentation on endoscopic images with point supervision

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BIOMEDICAL signal processing AND CONTROL 2024年 92卷

作者： Wei, Xiaoxiao Deng, Zhen Zheng, Xiaochun He, Bingwei Hu, Ying Fuzhou Univ Dept Mech Engn & Automat Fuzhou 350108 Peoples R China Fujian Med Univ Fujian Prov Hosp Dept Anesthesiol Shengli Clin Med Coll Fuzhou 350108 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

The ability to automatically segment anatomical targets on medical images is crucial for clinical diagnosis and interventional therapy. However, supervised learning methods often require a large number of pixel-wise labels that are difficult to obtain. This paper proposes a weakly supervised glottis segmentation (WSGS) method for training end-to-end neural networks using only point annotations as training labels. This method functions by iteratively generating pseudo-labels and training the segmentation network. An automatic seeded region growing (ASRG) algorithm is introduced to generate quality pseudo labels to diffuse point annotations based on network prediction and image features. Additionally, a novel loss function based on the structural similarity index measure (SSIM) is designed to enhance boundary segmentation. Using the trained network as its core, a glottis state monitor is developed to detect the motion behavior of the glottis and assist the anesthesiologist. Finally, the performance of the proposed approach was evaluated on two datasets, achieving an average mIoU and accuracy of 82.7% and 91.3%. The proposed monitor was demonstrated to be effective, which holds significance in clinical applications.

关键词： Weakly supervised learning Medical image segmentation Glottis segmentation

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