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Leveraging Multimodal Diffusion Models to Accelerate Imaging with Side Information

Leveraging Multimodal Diffusion Models to Accelerate Imaging...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Timofey Efimov Harry Dong Megna Shah Jeff Simmons Sean Donegan Yuejie Chi Electrical and Computer Engineering Carnegie Mellon University Materials and Manufacturing Directorate Air Force Research Laboratory

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have found phenomenal success as expressive priors for solving inverse problems, but their extension beyond natural images to more structured scientific domains remains limited. Motivated by applications in materials science, we aim to reduce the number of measurements required from an expensive imaging modality of interest, by leveraging side information from an auxiliary modality that is much cheaper to obtain. To deal with the non-differentiable and black-box nature of the forward model, we propose a framework to train a multimodal diffusion model over the joint modalities, turning inverse problems with black-box forward models into simple linear inpainting problems. Numerically, we demonstrate the feasibility of training diffusion models over materials imagery data, and show that our approach achieves superior image reconstruction by leveraging the available side information, requiring significantly less amount of data from the expensive microscopy modality.

关键词： Training Three-dimensional displays Inverse problems Microscopy Closed box Diffusion models Turning Data models Numerical models image reconstruction

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An Optimized CNN-LSTM Model for Detecting Cardiac Arrhythmias

An Optimized CNN-LSTM Model for Detecting Cardiac Arrhythmia...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Shahab Ul Hassan Said Jadid Abdulkadir Mohd Soper Mohd Zahid Abdul Muiz Fayyaz Safwan Mahmood Al-Selwi Ebrahim Hamid Sumiea Dept. of Computer & Information Sciences Cent. for Intelligent Signal&Imaging Research Universiti Teknologi PETRONAS Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Cardiac arrhythmia is one of the most critical cardiovascular diseases that cause millions of fatalities every year. Early detection of the disease by analyzing the electrocardiogram signals of patients has the potential to save many lives. Deep learning prediction models have gained a lot of attention for arrhythmia prediction. Among them, convolutional neural network (CNN) and long short-term memory (LSTM) techniques are widely used. These models have been recently combined into the CNN-LSTM model to achieve high accuracy and efficiency in arrhythmia prediction. However, there is a lack of studies analyzing the performance of CNN and CNN-LSTM techniques to find the optimal values for key parameters such as the number of filters, kernel size, and layers. This article determines optimized CNN (OCNN) and optimized CNN-LSTM (OCNN-LSTM) models for MIT-BIH arrhythmia datasets by analyzing the models' performance by varying several key parameters. Performance is measured in terms of accuracy, AUC, recall, precision, and specificity. Hence, the aim is to find the optimum values for the key parameters required to develop deep learning models for the MIT-BIH arrhythmia dataset. The finest outcomes attained for the OCNN and OCNN-LSTM models were 99.9% and 98.1% AUC, 99.0%, 96.1% accuracy, 98.5% and 93.3% recall, 98.1%, and 93.5% precision, and 99.2% and 97.2% specificity, respectively.

关键词： Deep learning Analytical models Accuracy Filters Arrhythmia signal processing algorithms Predictive models

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A Robust Large-scale Dataset for Assessing Light Field image Quality

A Robust Large-scale Dataset for Assessing Light Field Image...

引用

IEEE International conference on signal and image processing (ICSIP)

作者： Jian Ma Zhuochi Liu Yaozong xu Jiajun Yu Yang Wang Yuhao Liu School of Internet Anhui University Hefei China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. Deep learning methods, leveraging neural networks, have shown promising performance in image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field image Quality, named RLSD, specifically designed for evaluating the quality of light field images. The dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. The dataset is publicly available at the following link: “https://***/s/1kJmx4qsy8ywLPba-HwGCEg” (password: xY28).

关键词： image quality Three-dimensional displays image coding Statistical analysis Noise Neural networks Imaging

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CabiNet: A Deep Learning Framework for Multiclass Medical image Segmentation from Multiple Single Class Datasets

CabiNet: A Deep Learning Framework for Multiclass Medical Im...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Aman Soni Ishita Maiti Nirmalya Ghosh Electrical Engineering IIT Kharagpur Kharagpur India School of Medical Science and Technology IIT Kharagpur Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging for multi-class annotations and segmentation of regions of interest, e.g., multi-organ segmentation that plays a vital role in computer-assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multi-class healthy organ segmentation from partially annotated abdominal MRI data, i.e., multiple single-class datasets. Multiple organ specific expert networks and a Jack of All (JoA) network are trained to generate posterior probability maps of individual organs simultaneously which are accumulated to get the final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%.

关键词： Deep learning image segmentation Annotations Magnetic resonance imaging Posterior probability Surgery Benchmark testing Medical diagnostic imaging Kidney Tuning

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Deep Learning Model Development with U-net Architecture for Glottis Segmentation 29

Deep Learning Model Development with U-net Architecture for ...

引用

29th IEEE conference on signal processing and Communications applications (SIU)

作者： Derdiman, Yasar Said Koc, Turgay Suleyman Demirel Univ Elekt & Elekt Muhendisligi Bolumu Isparta Turkey

ISBN: (纸本)9781665436496

With the development of technology for detailed examination of the vibration of the vocal cords, the use of highspeed endoscopic camera images has become widespread. Studies conducted to segment the glottis, which is located between the vocal cords of particular interest, from these images use classical segmentation methods such as histogram, active contour, and region enlargement. In this study, a deep learning model was developed for glottis segmentation using the U-Net architecture, which has shown superior performance in biomedical image segmentation in recent years, and the performance of this model was developed on an IRCAM database consisting of 256x256 3000 images, which were manually labeled. It has been compared with classical segmentation methods. However, how the glottis size affects the segmentation performance was examined. As a result of the study, it was observed that system performances decreased as the glottal area size decreased, especially active contour performance decreased significantly, but this effect remained limited in the U-Net model. The developed model showed the highest performance with 0.88 sensitivity and 0.99 accuracy on average regardless of the size of the glottal area. In terms of recall, the histogram-based method yielded the highest result with 0.96. In terms of DICE, U-Net gave the best result with a score of 0.83.

关键词： image processing deep learning U-Net glottis segmentation

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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A Semi-supervised Low-Light image Enhancement with Color Guidance

A Semi-supervised Low-Light Image Enhancement with Color Gui...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Yuxin Wang Shuolin Yang Qianxi Wu Zhishuo Zhang Yunxia Liu Yang Yang Yakui Dong Cheng Fei Junliang Liu Lili Wang Shuzhen Fan Yongfu Li Center for Optics Research and Engineering (CORE) Shandong University Qingdao China Key Laboratory of Laser & Infrared System Ministry of Education Shandong University Qingdao China School of Information Science and Technology Shandong University Qingdao China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Low-light image enhancement (LLIE) has found extensive applications in surveillance and related fields. Current deep learning-based algorithms have demonstrated significant advantages over traditional methods for LLIE. However, the issue of color distortion still remains a challenge. This paper proposes a semi-supervised curve estimation and restoration network that consists of two cascaded sub-networks with distinct functionalities. The first-level sub-network applies specific constraints to enhance different regions based on their brightness levels. The second-level sub-network is trained using superpixel color consistency loss and global histogram loss to effectively correct color distortion. Experimental results demonstrate that the proposed method achieves satisfactory outcomes in both visual quality and evaluation metrics.

关键词： Measurement Histograms Visualization image color analysis Surveillance Brightness Estimation Distortion image restoration image enhancement

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Consistent 3D Medical image Inpainting via Implicit Representation and Multi-View Sampling

Consistent 3D Medical Image Inpainting via Implicit Represen...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Yuqi Hu Zhenrong Shen Zhiyun Song Dongdong Chen Liao Wang Lichi Zhang School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Orthopaedic Surgery Shanghai Ninth People's Hospital Shanghai China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Medical image inpainting plays a crucial role in various clinical applications, such as metal artifact removal, surgical planning, and anomaly segmentation. However, existing methods often operate on 2D image slices when inpainting 3D images, leading to inter-slice discontinuity and an inability to fully utilize the 3D structural information. To address these issues, we propose a novel 3D inpainting network leveraging Implicit Representations and Multi-View Sampling (IRMVS-3D). IRMVS-3D harnesses the comprehensive 3D information by multi-scale Masked AutoEncoder(MAE) guided encoding, and aggregating global and local features via Fast-Fourier-Convolution(FFC) blocks. To tackle the inter-slice discontinuity problem, IRMVS-3D treats the entire 3D image as a continuous implicit function, predicting voxel intensities through implicit representation. Additionally, to improve training efficiency while ensuring the continuity, we introduce a multi-view sampling strategy. During inference, intensities of all voxels are gathered to reconstruct the complete 3D volume. Experimental results demonstrate that our method achieves state-of-the-art performances in inpainted image quality on each view, while effectively addressing the inter-slice discontinuity problem.

关键词： Training image quality image segmentation Three-dimensional displays Surgery Metals signal processing Planning image reconstruction Biomedical imaging

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3rd International conference on VLSI, Communication and signal processing, VCAS 2020

3rd International Conference on VLSI, Communication and Sign...

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3rd International conference on VLSI, Communication and signal processing, VCAS 2020

ISBN: (纸本)9789811627606

The proceedings contain 108 papers. The special focus in this conference is on VLSI, Communication and signal processing. The topics include: Low-Power Enhanced Speed Two-Tail Dynamically Controlled Comparator Suitable for Subthreshold CMOS Circuits;Charge Plasma-Based Tunnel FET with Enhanced DC Performance Applicable for Ultra-low Power applications;data Collection Website with Brain Tumor and Pneumonia Detection;design and Comparison of Low-Power, High-Speed T Flip Flop, and 4-Bit Asynchronous Counter Using Various Design Techniques;Design of Ku Band HEMT-Based Class AB Amplifier;preview Control for Discrete Time Systems;Design of 15-4 Compressor for DSP Application;UWB Antenna for ITS Application;FPGA-Based Modulation Technique for Five-to-Three-Phase Ultra Sparse Matrix Converter;Exact SER for Modulation Schemes Over Generalized Rician Fading Channel;Implementation of an Efficient Handover Algorithm on AP Using Software-Defined Wi-Fi Systems;a Novel Technique for the Design and Implementation of a 4 × 2 All Optical Encoder Using Micro-ring Resonator Switching Mechanism;design of Micro-ring Resonator Based All-Optical Half Adder Using 2–4 Line Decoder Circuit;image Restoration of Landslide Photographs Using SRCNN;classification Techniques for Binary Motor imagery signal for Brain-Computer Interfaces;x Band Class F Power Amplifier for Satellite Communication;Investigation of GNR Based Meta-Material Antenna for Single and Dual Band THz applications;Comparative Study of Silicon and In0.53Ga0.47As-Based Gate-All-Around (GAA) MOSFETs;A Simple and Sturdy Hybrid Interference Canceller for a DS-CDMA System in Multipath Environment for Static and Mobile Users;Performance Evaluation of SPR Sensor on Using Graphene/TMDCs in Visible and Near Infrared Wavelength Regime;consensus Mechanism for Peer-to-Peer Energy Trading.

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Computationally Efficient image Correlation for De-blurring with Photon-Counting Instruments

Computationally Efficient Image Correlation for De-blurring ...

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IEEE Aerospace conference (AeroConf)

作者： Koets, Michael A. Roming, Peter W. A. Southwest Res Inst Space Sci & Engn 6220 Culebra Rd San Antonio TX 78238 USA

ISBN: (纸本)9781728174365

We present a new implementation of an image alignment and de-blurring algorithm for astronomical observations with photon-counting imagers. The algorithm substantially corrects the effects of observatory attitude changes over long observations and enables the reconstruction of images with superior resolution to those generated without alignment processing. The performance of the algorithm is quantified via simulations of a realistic observation scenario. We formulate a novel, computationally efficient implementation of the alignment algorithm that enables its use for applications with large image sizes and limited computational hardware. Such computational efficiency is critical for spaceflight applications where extreme environments limit the availability of computational hardware to components several generations behind the state of the art. We present an implementation of a Verilog realization of the alignment algorithm which is suitable for execution on a space-qualified Field Programmable Gate Array.

关键词： Observatories image resolution Instruments signal processing algorithms Logic gates Hardware Computational efficiency

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