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Autoregressive models for biomedical signal processing

Autoregressive models for biomedical signal processing

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Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Jonas F. Haderlein Andre D. H. Peterson Anthony N. Burkitt Iven M. Y. Mareels David B. Grayden Dept of Biomedical Engineering University of Melbourne Australia Dept of Medicine St Vincent’s Hospital University of Melbourne Australia Federation University Australia and Honorary Professor at University of Melbourne Australia Graeme Clark Institute University of Melbourne Australia

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system *** relevance— This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy.

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Weakly-Supervised Scene-Specific Crowd Counting Using Real-Synthetic Hybrid Data

Weakly-Supervised Scene-Specific Crowd Counting Using Real-S...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yaowu Fan Jia Wan Yuan Yuan Qi Wang School of Software Northwestern Polytechnical University Xi’an Shaanxi P. R. China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) Northwestern Polytechnical University Xi’an Shaanxi P. R. China Department of Electrical and Computer Engineering University of California San Diego USA

Due to the domain gap between the public large-scale datasets and actual scenes, the crowd counting models trained on the common datasets have a significant performance degradation when applying in practical applications. To address the above issue, one of the solution is to label additional data from the novel scenes, which is time-consuming and impractical for multiple scenes. Another solution is to utilize domain adaptation approaches to adapt a well-trained model to novel scenes. However, most of these approaches focus on appearance adaptation while the background and the crowd distribution is not adapted. In this paper, we propose a weakly-supervised method with real-synthetic hybrid data which only requires a small portion of unlabelled real images and auto-generated synthetic labelled images for training. First, the hybrid data is generated based on background from the real scene and random distributed synthetic persons. Second, an initialized counter is trained based on the hybrid data and the crowd distribution is predicted based on the predictions on real images. Then, a better crowd counter is trained based on new hybrid data generated from updated crowd distribution. The process is iterated until convergence. Extensive experiments demonstrate the effectiveness of the proposed method.

关键词： Training Degradation Adaptation models Signal processing algorithms Distributed databases Signal processing Hybrid power systems

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image Registration for Virtual Drone Flight Simulation

Image Registration for Virtual Drone Flight Simulation

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Electrical, computer and Energy Technologies (ICECET), international conference on

作者： Konrad Koniarski Andrzej Myślinski Systems Research Institute Polish Academy of Sciences Warsaw Poland

In the past decade, drones have expanded their applications, such as surveying, search and rescue, and last-mile delivery. Drone autonomy is a growing focus in these areas, requiring test environments for both drone operators and autonomous algorithms. This work addresses the creation of a realistic test environment for accurate position perception, motion planning, and drone control. Our approach uses RGB-D sensors and image registration techniques to construct a 3D map of the real environment, enabling drone navigation simulation. We adapt the Iterative Closest Point (ICP) algorithm, incorporating feature point filtering and local optical flow to expedite 3D point processing, particularly in static scenes. A challenge in the standard ICP algorithm is selecting suitable initial points for effective matching between consecutive point clouds. Our modified approach tackles this issue, optimizing the algorithm for 3D map construction. Since our application involves RGB-D image sequences with minimal variation between successive key frames, we use a scale-space pyramid and FAST techniques to select a limited number of key points, reducing the computational load. Moreover, by employing a local optical flow-based point-matching approach, we eliminate resource-intensive operations in each ICP optimization iteration. We demonstrate the practical utility of our method through a virtual 3D drone flight simulator.

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Investigating Intensity Normalisation for PET Reconstruction with Supervised Deep Learning

Investigating Intensity Normalisation for PET Reconstruction...

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ieee Symposium on Nuclear Science (NSS/MIC)

作者： I. R.D. Singh A. Denker B. Jin K. Thielemans S. Arridge Department of Computer Science University College London London United Kingdom Center of Industrial Mathematics Bremen University of Bremen Bremen Germany Department of Mathematics The Chinese University of Hong Kong Hong Kong China Institute of Nuclear Medicine University College London London United Kingdom Centre for Medical Image Computing University College London London United Kingdom

Deep learning methods have shown great promise in the field of Positron Emission Tomography (PET) reconstruction, but the successful application of these methods depends heavily on the intensity scale of the images. Normalisation is a crucial step that aims to adjust the intensity of network inputs to make them more uniform and comparable across samples, acquisition times, and activity levels. In this work, we study the influence of different linear intensity normalisation approaches. We focus on two popular deep learning based image reconstruction methods: an unrolled algorithm (Learned Primal-Dual) and a post-processing method (OSEMConvNet). Results on the out-of-distribution test dataset demonstrate that the choice of intensity normalisation significantly impacts on generalisability of these methods. Normalisation using the mean of acquisition data or corrected acquisition data led to improved peak-signal-to-noise-ratio (PSNR) and data-consistency (KLDIV). Through evaluation of lesion-specific metrics of contrast recovery coefficients (CRC) and standard deviation (STD) an increase in CRC and STD is observed. These findings highlight the importance of carefully selecting an appropriate normalisation method for supervised deep learning-based PET reconstruction applications.

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Point-Based 3D Virtual Fixture Generating for image-Guided and Robot-Assisted Surgery in Orthopedics*

Point-Based 3D Virtual Fixture Generating for Image-Guided a...

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ieee/ASME (AIM) international conference on Advanced Intelligent Mechatronics

作者： Teng Li Armin Badre Hamid D. Taghirad Mahdi Tavakoli Department of Electrical and Computer Engineering Faculty of Engineering University of Alberta Edmonton Canada Western Upper Limb Facility Sturgeon Hospital St. Albert Canada Division of Orthopaedic Surgery Department of Surgery Faculty of Medicine & Dentistry University of Alberta Edmonton Canada Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Virtual fixture (VF) has been playing a vital role in robot-assisted surgeries, such as guiding surgical tools’ movement and protecting a beating heart. In orthopedic surgery, preplanned images are often used in the operating room, on which planning curves might be drawn, for instance, to mark out the boundaries for osteophytes to be removed. These curves can be used to generate VF to assist in removing osteophytes during the operation. A challenge is that the hand-drawn curves usually have a random shape and cannot be mathematically represented by equations, thus most of the existing algorithms will not work in this scenario. In this paper, an algorithm of VF generating based on point clouds is presented, with which VF can be generated directly from cloud points, for example, point clouds of hand-drawn curves extracted from an image. The effectiveness of the VF algorithm is evaluated by a series of simulations and experiments. The VF algorithm is also tested in an image-based scenario and its effectiveness is demonstrated. The presented point-based VF algorithm is promising to be used in various applications in image-guided surgery to generate VF for objects with various shapes.

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InterFormer Real-time Interactive image Segmentation

InterFormer Real-time Interactive Image Segmentation

引用

international conference on computer Vision (ICCV)

作者： You Huang Hao Yang Ke Sun Shengchuan Zhang Liujuan Cao Guannan Jiang Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Intelligent Manufacturing Department Contemporary Amperex Technology Co. Limited (CATL)

Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators’ later click is based on models’ feedback of annotators’ former click. This serial interaction is unable to utilize model’s parallelism capabilities. Second, in each interaction step, the model handles the invariant image along with the sparse variable clicks, resulting in a process that’s highly repetitive and redundant. For efficient computations, we propose a method named InterFormer that follows a new pipeline to address these issues. In-terFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to prepro-cess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. Furthermore, the I-MSA module’s deployment on low-power devices extends the practical application of interactive segmentation. The I-MSA module utilizes the preprocessed features to efficiently response to the annotator inputs in real-time. The experiments on several datasets demonstrate the effectiveness of Inter-Former, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices. The code is available at https://***/YouHuang67/InterFormer.

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Medical image Segmentation Using Dual Branch Networks with Embedded Attention Mechanism

Medical Image Segmentation Using Dual Branch Networks with E...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuaishuai Yang Min Jin Lian Wang Changcheng Lu Yajie Meng Die Yan Zhijian Huang Junlin Xu College of Computer Science and Electronic Engineering Hunan University Changsha China

Medical applications heavily rely on medical image segmentation for tasks such as disease detection, treatment planning, and surgical navigation. While CNNs excel at local feature extraction, they have limitations in capturing global features. Transformer-based models are effective in global feature extraction but struggle with local features. TransUnet combines CNN and Transformer but lacks sufficient global and local feature extraction due to its serial connection approach. TransFuse addresses this by designing parallel Transformer and CNN branches for global and local feature extraction. The self-attention of ViT, the core network of the Transformer branch, has a computational complexity that is directly correlated with the square of the image size, which means it cannot meet the need for multi-level information in complicated segmentation tasks. To overcome these limitations, we propose SR-Unet, a novel dual-branch medical image segmentation network. It merges Transformer and CNN branches in the encoder for optimal global and local feature extraction, utilizing the Swin Transformer as the Transformer branch backbone. Furthermore, we integrate CBAM(Convolutional Block Attention Module) in the decoder’s fusion block to fully merge global and local features and eliminate redundant and irrelevant information, thereby enhancing segmentation accuracy. Experimental results on the Synapse, CXML, and BUSI datasets demonstrate that SR-Unet outperforms TransFuse. It achieves higher DSC metrics by 3.47%, 0.16%, and 0.35%, and significantly better HD metrics by 41.21%, 6.98%, and 11.33%.

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Approx-SMOTE Federated Learning Credit Card Fraud Detection System

Approx-SMOTE Federated Learning Credit Card Fraud Detection ...

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ieee Annual international computer Software and applications conference (COMPSAC)

作者： Jiacheng Wang Wenzheng Liu Yifei Kou Dicheng Xiao Xiaofeng Wang Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway Changsha University of Science and Technology Changsha China College of Computer National University of Defense Technology Changsha China

Credit fraud brings billions of dollars to banks every year. However, the existing research on fraud detection methods has encountered bottlenecks. Traditional fraud detection models are difficult to improve performance and accuracy in detecting fraudulent transactions. The main reason is that the bank credit dataset is very distorted, and the proportion of positive and negative samples is seriously unbalanced, and secondly, due to data privacy and security issues, the dataset is usually not allowed to be shared between different users. In this paper, we improve and enhance the existing credit card fraud detection system, and propose Approx-SMOTE federated learning credit card fraud detection system (AFLCS), through the method of salting and interference items in the data balancing and learning module, design a federal credit fraud detection algorithm for the data after data balance, and improve the network structure of CNN and learning module, the AFLCS can not only improve the processing time by nearly 30 times without affecting the performance, and even better the privacy and security of the information system, but also reserve space for the subsequent application of the system to data expansion between different banks. We achieved our design on the pysyft framework and tested it with an approved data set. The experiments show that our indicators are higher than the existing solutions.

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Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed Algorithm

Kidney Stone Detection from CT images using Probabilistic Ne...

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Advances in Intelligent Computing and applications (AICAPS), international conference on

作者： Sabitha Rani B. S Malu G Elizabeth Sherly School of Computer Science Digital University Kerala(DUK) Kerala India Digital University Kerala(DUK) Kerala India

kidney stones scientifically known as renal calculus or nephrolith consist of dense crystal masses generally originate in the kidneys and pass through the urinary tract which includes urethra, bladder and ureters. Genetic, dietary and environmental causes can be associated with the occurrence and severity of kidney stones. Imaging studies play a vital part in the treatment of kidney stone patients. CT is a precise diagnostic procedure for gastrointestinal illnesses. In essence, CT sends x-rays in small pieces that are saved on the screen as photographs from the body. The proposed method involves the diagnosis of kidney stones using image processing techniques such as pre-processing,segmentation,feature extraction and classification etc. In the initial stage the salt and pepper noise is removed by using a 3x3 median filter and discrete wavelet transform (DWT).K-Means clustering algorithm is used after segmenting the kidney stones using the watershed segmentation algorithm. The key objective of this study is to extract the features of segmented kidney stones by using the Grey level co-occurrence matrix(GLCM) and classify it using Probabilistic Neural Network (PNN) .The results we got indicate that 194 and 107 as the maximum sensitivity and maximum specificity point which was higher than the conventional renal calculus detection *** our proposed framework achieves an overall accuracy of 86.8%.

关键词： image segmentation Computed tomography Clustering algorithms Watersheds X-rays Feature extraction Probabilistic logic

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ImagineNet: Target Speaker Extraction with Intermittent Visual Cue Through Embedding Inpainting

ImagineNet: Target Speaker Extraction with Intermittent Visu...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zexu Pan Wupeng Wang Marvin Borsdorf Haizhou Li Institute of Data Science National University of Singapore (NUS) Singapore Department of Electrical and Computer Engineering NUS Singapore Machine Listening Lab University of Bremen Germany Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility.

关键词： Visualization Lips Design methodology Signal processing algorithms Signal processing Noise robustness Synchronization

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