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PolSAR image Classification Based on Multi-Modal Contrastive Fully Convolutional Network

REMOTE SENSING 2024年第2期16卷 296页

作者： Hua, Wenqiang Wang, Yi Yang, Sijia Jin, Xiaomin Xian Univ Posts & Telecommun Sch Comp Sci & Technol Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710061 Peoples R China

deep neural networks have achieved remarkable results in the field of polarimetric synthetic aperture radar (PolSAR) image classification. However, PolSAR is affected by speckle imaging, resulting in PolSAR images usually containing a large amount of speckle noise, which usually leads to the poor spatial consistency of classification results and insufficient classification accuracy. Semantic segmentation methods based on deep learning can realize the task of image segmentation and classification at the same time, producing fine-grained and smooth classification maps. However, these approaches require enormous labeled data sets, which are laborious and time-consuming. Due to these issues, a new multi-modal contrastive fully convolutional network, named MCFCN, is proposed for PolSAR image classification in this paper, which combines multi-modal features of the same pixel as inputs to the model based on a fully convolutional network and accomplishes the classification task using only a small amount of labeled data through contrastive learning. In addition, to describe the PolSAR terrain targets more comprehensively and enhance the robustness of the classifier, a pixel overlapping classification strategy is proposed, which can not only improve the classification accuracy effectively but also enhance the stability of the method. The experiments demonstrate that compared with existing classification methods, the classification results of the proposed method for three real PolSAR datasets have higher classification accuracy.

关键词： contrastive learning fully convolutional network (FCN) polarimetric synthetic aperture radar (PolSAR) image classification

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BIOMEDICAL SIGNAL processing AND CONTROL 2024年 89卷

作者： Mahmood, Md Junaid Raj, Pranaw Agarwal, Divyansh Kumari, Suruchi Singh, Pravendra Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee India Flipkart Bengaluru India Amazon Bengaluru India Microsoft Bengaluru India

Medical image classification presents significant challenges due to limited labeled samples and class imbalance resulting from varying disease prevalence. In many real-world scenarios, a scarcity of labeled images pertaining to specific ailments coexists with an abundance of unlabeled ones. The process of annotating these unlabeled medical images by domain experts is both time-intensive and financially burdensome. Consequently, medical image classification remains an active and evolving research area. Semi-supervised learning (SSL) techniques hold promise in addressing these challenges by effectively leveraging both labeled and unlabeled data. However, the application of SSL to medical image classification necessitates overcoming two primary obstacles: (1) the reliable estimation of pseudo-labels for unlabeled images and (2) the mitigation of biases stemming from class imbalance. In this work, we introduce a novel semi-supervised learning approach, denoted as SPLAL, which adeptly addresses these challenges. SPLAL leverages the concept of class prototypes and employs a weighted combination of classifiers to predict dependable pseudo-labels for a subset of unlabeled medical images. Additionally, we introduce an alignment loss term designed to alleviate model biases, particularly those arising from the overrepresentation of majority classes. This alignment loss mechanism ensures the model's consistency in delivering the same classification output for diverse augmented images of the same disease. To evaluate the effectiveness of our proposed approach, we conducted comprehensive experiments on two publicly accessible benchmark datasets for medical image classification. Our empirical findings unequivocally demonstrate that SPLAL outperforms several state-of-the-art semi-supervised learning methods across various evaluation metrics.

关键词： Medical image classification Semi-supervised learning Medical imaging deep learning Machine learning

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deep learning in MRI-guided radiation therapy: A systematic review

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JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS 2024年第2期25卷 e14155页

作者： Eidex, Zach Ding, Yifu Wang, Jing Abouei, Elham Qiu, Richard L. J. Liu, Tian Wang, Tonghe Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA USA Emory Univ Winship Canc Inst Atlanta GA USA Georgia Inst Technol Sch Mech Engn Atlanta GA USA Icahn Sch Med Mt Sinai Dept Radiat Oncol New York NY USA Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY USA Emory Univ Sch Med Dept Radiat Oncol 1365 Clifton Rd NE Atlanta GA 30322 USA

Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models.

关键词： deep learning MRI-guided radiation therapy radiotherapy review

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Patient-specific cerebral 3D vessel model reconstruction using deep learning

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第10期62卷 3225-3232页

作者： Koizumi, Satoshi Kin, Taichi Shono, Naoyuki Kiyofuji, Satoshi Umekawa, Motoyuki Sato, Katsuya Saito, Nobuhito Univ Tokyo Hosp Dept Neurosurg 7-3-1 Bunkyo Ku Tokyo 1138655 Japan Univ Tokyo Grad Sch Med Dept Med Informat Engn Tokyo Japan

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing.

关键词： deep learning Medical image processing Magnetic resonance angiography Aneurysm Segmentation

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deep learning-based image analysis for filamentous and floc-forming bacteria in wastewater treatment

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JOURNAL OF WATER PROCESS ENGINEERING 2024年 65卷

作者： Al-Ani, Sama Guo, Hui Fyfe, Sheila Long, Zebo Donnaz, Sylvain Kim, Younggy McMaster Univ Dept Civil Engn Hamilton ON L8S 4L8 Canada Veolia Water Technol & Solut Oakville ON L6M 4B2 Canada

In municipal wastewater treatment, effective secondary clarification relies on the balance between floc-forming bacteria and filamentous bacteria. Consequently, comprehensive and real-time monitoring of this balance will enable reliable operation of biological wastewater treatment. This research presents an artificial intelligence (AI)-based approach for the classification of filamentous and floc-forming bacteria in microscopic images using deep learning. To provide ground truth labeling, an automated rule-based segmentation algorithm was developed using color and morphology criteria along with supplementary filtration steps to enhance the precision of filamentous and floc-forming bacteria identification. The segmentation algorithm demonstrated reliable detection and categorization of bacteria across varying background intensities and effectively recognized intricate microbial configurations. Subsequently, the supervised deep learning model was trained on the segmented images and constructed with an encoder/decoder architecture. Machine training with only 68 microscopic images demonstrated successful classification of the filamentous and floc-forming bacteria with a 97.8 % accuracy. In addition, qualitative evaluation demonstrated that the deep learning model could generalize machine understanding across diverse scenarios and discern misclassified filamentous bacteria accurately. The proposed model stands as a promising automated tool for real-time quantification of filamentous and floc-forming bacteria in bioreactors and clarifiers, offering the potential for reliable operation as well as immediate actions for sludge bulking and membrane fouling problems.

关键词： deep learning Sludge bulking image segmentation Filamentous bacteria Floc-forming bacteria

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A real-time Demonstrator for image Classification using FPGA-based Logic Neural Networks

A Real-time Demonstrator for Image Classification using FPGA...

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Conference on real-time processing of image, Depth, and Video Information

作者： Concha, David Garcia-Espinosa, Francisco J. Ramirez, Ivan Alberto Aranda, Luis Univ Rey Juan Carlos CAPO Res Grp Madrid 28933 Spain

ISBN: (纸本)9781510673199;9781510673182

real-time image processing is a key area of focus, but computationally intensive. Neural networks effectively address classification tasks, but they are not always a viable option, particularly in environments where high power consumption or computational requirements are limiting factors. Hardware devices such as Field-Programmable Gate Arrays (FPGAs) offer significant parallelization capabilities that can be fully exploited when the implemented circuit is composed solely of logic gates. In addition, FPGAs are also interesting alternatives to traditional GPU-based implementations in terms of power consumption and reconfiguration capabilities. They can be used as a demonstration platform to validate a hardware design that can be later manufactured, creating the final Application-Specific Integrated Circuit (ASIC). This paper introduces a practical demonstration platform based on an FPGA that highlights the great capabilities of logic neural networks, a type of neural network constructed exclusively with logic gates. By harnessing FPGA parallelization and logic gates, we have achieved a balance between computational power and real-time performance. This approach ensures that image classification occurs at speeds on the order of nanoseconds. This ultra-fast processing is well-suited for real-time image analysis applications across various domains. Industries that rely on quality control, such as manufacturing, will benefit from rapid and precise assessments. In the field of medical image processing, where quick diagnoses are crucial, this technology promises transformative advancements. The demonstration platform developed serves as a proof of concept for logic neural networks, offering a solution to the challenge of real-time image processing and representing the first step towards the implementation of future architectures of logic networks in hardware.

关键词： deep learning Field-Programmable Gate Array (FPGA) Logic Neural Network (LNN) real-time image Classification

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Semi-supervised learning for real-time anomaly detection in pulsed transfer wire arc additive manufacturing

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JOURNAL OF MANUFACTURING PROCESSES 2024年 128卷 84-97页

作者： Mattera, Giulio Polden, Joseph Caggiano, Alessandra Nele, Luigi Pan, Zengxi Norrish, John Univ Naples Federico II Dept Chem Mat & Ind Prod Engn Naples Italy Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW Australia Univ Naples Federico II Fraunhofer Joint Lab Excellence Adv Prod Technol F Naples Italy Univ Naples Federico II Ctr Adv Metrol Technol Serv CeSMA Naples Italy

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such

关键词： Wire arc additive manufacturing Anomaly detection Machine learning Frequency domain image processing

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RT-SNDETR: real-time supernova detection via end-to-end image transformers

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2024年第1期534卷 726-737页

作者： Pan, Zhi-Ren Qiu, Bo Li, Guang-Wei Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianfu Yongxing Lab Chengdu 610213 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Natl Astron Observ CAS Key Lab Opt Astron Beijing 100101 Peoples R China

In large-scale astronomical surveys, traditional supernova detection pipelines rely on complex and relatively inefficient image differencing techniques. This paper proposes an end-to-end deep-learning supernova detection network, the real-time SuperNova DEtection TRansformer (RT-SNDETR). This network partially replaces traditional pipelines by integrating image differencing, source detection, and real-bogus classification, achieving a speed 51.49 times that of the fastest image differencing method, SFFT. Additionally, it remains competitive with methods like YOLO v8, offering a well-balanced trade-off between speed and accuracy. Experimental results highlight RT-SNDETR's superior performance, with an average precision(AP) of 96.30 per cent on synthetic samples and 76.60 per cent on real supernova samples. It significantly outperforms other detection networks, including RT-DETR (+5.6 per cent AP on synthetic/+5.1 per cent AP on real samples) and Cascade R-CNN (+8.9 per cent AP on synthetic/ +28.6 per cent AP on real samples). The incorporation of CycleGAN-based data generation methods plays a significant role in enhancing RT-SNDETR's performance. These methods simulate realistic PSF variations, enabling the object detection network to learn more robust features and improving its generalization to real supernovae data. Additionally, by integrating unsupervised domain adaptation techniques, RT-SNDETR achieves an AP of 81.70 per cent on real SDSS supernova survey samples. This study demonstrates RT-SNDETR's potential to significantly enhance both the speed and accuracy of supernova detection, making it a highly effective solution for large-scale astronomical surveys.

关键词： methods: data analysis techniques: image processing techniques: photometric transients: supernovae

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Detection of False Data Injection Attacks in Distribution Networks: A Vertical Federated learning Approach

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IEEE TRANSACTIONS ON SMART GRID 2024年第6期15卷 5952-5964页

作者： Kesici, Mert Pal, Bikash Yang, Guangya Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Tech Univ Denmark Dept Wind & Energy Syst DK-2800 Kongens Lyngby Denmark

This paper proposes a collaborative learning framework based on vertical federated learning for detecting false data injection attacks in distribution networks. The proposed framework empowers entities that are responsible for a sub-network to collaboratively construct an FDIA detection model, effectively addressing issues associated with data sharing and enabling the utilization of various measurements from each sub-network. The proposed framework enables real-time collaboration between the server and the grid edge-side by allocating the two models created through the split learning approach applied to the proposed attention-based hybrid deep learning model. The grid edge-side is tasked with extracting spatial features, while the server is responsible for extracting temporal features from the data processed by the grid edge-side. The edge-side model is designed by adopting an attention module integrated into a deep learning model while the server-side model is designed based on the Bi-LSTM model. The effectiveness of the proposed framework is demonstrated on the IEEE 123 and IEEE 37 node test systems.

关键词： Feature extraction Distribution networks Data models Federated learning real-time systems Particle measurements image edge detection False data injection attack vertical federated learning cyber attack detection state estimation split learning

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An Intelligent Apple Identification Method via the Collaboration of YOLOv5 Algorithm and Fast-Guided Filter Theory

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第11期33卷

作者： Zhang, Eryue Zhang, He Coll Mech & Elect Engn Henan Vocat Coll Appl Technol Zhengzhou 450000 Peoples R China Henan Coll Surveying & Maping Dept Surveying & Mapping Engn Zhengzhou 450000 Peoples R China

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot's identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved;K-means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size;BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection.

关键词： Object recognition deep learning YOLOv5 fast-guided filter image processing

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