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IEEE International Symposium on Biomedical Imaging

作者： Liming Wu Alain Chen Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indiana USA Division of Nephrology School of Medicine Indiana University Indianapolis Indiana USA

Three-dimensional tissue cytometry is an important technique for quantitative analysis of cell structures in large fluorescence microscopy volumes. Accurate nuclei detection and segmentation is an important step for 3D tissue cytometry. Deep learning methods have shown promising results for nuclei detection and segmentation. However, manually annotating ground truth for training deep learning methods is labor-intensive and not practical for large 3D volumes. In this paper, we propose a 3D nuclei synthesis method, known as 3DSpCycleGAN, for generating 3D ground truth volumes along with corresponding synthetic microscopy volumes. Experimental results using fluorescence microscopy volumes demonstrate that our method generates more realistic 3D volumes when evaluated both visually and quantitatively than previously reported. We also show that using the synthetic volumes generated by 3DSpCycleGAN as training data improves segmentation accuracy for deep learning segmentation techniques.

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Anomaly Detection in Brain Tumor Imaging: Few-Shot Learning with Generative Models and Knowledge Transfer

Anomaly Detection in Brain Tumor Imaging: Few-Shot Learning ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Khodabakhsh A. Aghaee N. Niroomand H. Arabi H. Zaidi School of Electrical Engineering Sharif University of Technology Biomedical Signal and Image Processing Laboratory (BiSIPL) Tehran Islamic Republic of Iran School of Electrical and Computer Engineering Toronto Metropolitan University Car-diovascular Imaging Modeling and Biomechanics Group Toronto Ontario Canada Division of Nuclear Medicine & Molecular Imaging Genève Geneva University Hospital Genève Switzerland

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Anomaly detection plays a vital role in the early identification of brain tumors in MRI scans, and it directly impacts diagnostic accuracy and patient outcomes. Despite its importance, current methods often fall short in handling sparse labeled data and precisely localizing anomalies. In this study, an innovative method that integrates Generative Adversarial Networks (GANs) with few-shot learning and transfer learning techniques, offering a new perspective in handling scarce labeled data in medical image analysis. At the core of the proposed method is a Vision Transformer-based generator, showcasing the dedication to advancing medical diagnostics technology. This generator, paired with a uniquely adapted discriminator benefiting from a pre-trained VGG16 network, enhances the model’s efficiency and accuracy in anomaly detection. The efficacy of the proposed method is demonstrated through its ability to distinguish between normal and patho- logical brain images obtained from a public dataset. In our study, the model achieved anomaly scores of approximately $0.14 \pm 0.18$ for normal images and $0.23 \pm 0.76$ for abnormal images. The enhanced precision achieved in anomaly detection and localization signifies a notable advancement beyond current methodologies. These outcomes pave the way for the creation of increasingly sophisticated and dependable diagnostic instruments, thereby facilitating more precise detection of brain tumors.

关键词： Adaptation models Accuracy Transfer learning Transformer cores Brain modeling Transformers Generators

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Overview of EMG signal Preprocessing and Classification for Bionic Hand Control

Overview of EMG Signal Preprocessing and Classification for ...

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Information Technologies & Smart Industrial Systems (ITSIS), IEEE

作者： Monaam Ayachi Hassene Seddik Electrical department RIFTSI research laboratory: intelligent robotics reliability and image signal processing Higher National Engineering School of Tunis (ENSIT) University of Tunis Tunis

This paper deals with the field of Biological signal especially one of the most used signals in the field of prosthetic devices, which is the EMG signal. After introducing this kind of signal, we will talk about different techniques used for preprocessing it in order to prepare it for classification tasks. We Will then overview different noises that affect those sensible type of signals, different techniques applied for filtering them and the useful envelope detection methods that can be applied on EMG. The next part of this paper will look at some famous feature extraction and classification techniques that proved themselves useful when dealing with classification tasks of biological signals. Finally, we will present the mechanical design of a multiarticulate multifunctional prosthetic hand, the promising classification results that can gives 98.3% and our perspectives.

关键词： Support vector machines Filtering Shape Feature extraction Electromyography Biology Noise measurement

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Soumeya Ourabia Nadia Baaziz Youcef Smara Departement of Electronics University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Tipaza Algeria Department of Computer Science and Engineering Research Laboratory in Multimedia Information University of Quebec in Outaouais - UQO Gatineau Québec Canada Laboratory of Image Processing and Radiation Faculty of electrical engineering University of Sciences and Technology Houari Boumediene -USTHB Algiers Algeria

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods.

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Can We Distinguish Genuine Minutiae from Imposter Minutiae 17

Can We Distinguish Genuine Minutiae from Imposter Minutiae

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17th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2020

作者： Phromsuthirak, Krisada Areekul, Vutipong Faculty of Engineering Kasetsart University Kasetsart Signal Image Processing Laboratory Department of Electrical Engineering Bangkok10900 Thailand

ISBN: (纸本)9781728164861

we propose a novel method to differentiate genuine minutiae from impostor minutiae of an enhanced latent fingerprint. Because poor-quality latent fingerprints are deposited on uncontrolled backgrounds with noises, the imposter minutiae are incorrectly detected by a conventional fingerprint enhancement process. As a result, these imposter minutiae decrease identification performance. We introduce a histogram of the spectrum difference between the original latent fingerprint and its enhanced image. This new feature is suitable for distinguishing between genuineness and imposter. The minutiae set from the NIST SD27 latent fingerprint database is used as ground truth to evaluate the performance of the proposed method and the existing quality assessment methods. The experimental results show that the proposed method outperforms the current methods in terms of precision and F-measure. © 2020 IEEE.

关键词： image enhancement

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Reduced Neural Network Based Ensemble Approach for Fault Detection and Diagnosis of Wind Energy Converter Systems

SSRN

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SSRN 2022年

作者： Mansouri, Majdi Dhibi, Khaled Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Electrical and Computer Engineering Program Texas A&M University Qatar Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

Fault Detection and Diagnosis (FDD) is very important to achieve the best operation and ensure the safe and continuous operation of wind energy conversion (WEC) systems. Over the last decade, artificial neural networks surpass classical machine learning models in a variety of FDD tasks. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. The main idea behind this proposal is to determine variable relationships via a data-reduction framework using Hierarchical K-means (H-K-means) clustering method in order to overcome the problem of computation time and storage cost. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other ensemble and single machine learning methods to illustrate and validate the advantages of the proposed techniques. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Direct Multipath-Based SLAM

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IEEE Transactions on signal processing 2025年

作者： Liang, Mingchao Leitinger, Erik Meyer, Florian University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States Graz University of Technology Signal Processing and Speech Communication Laboratory Graz8010 Austria University of California San Diego Scripps Institution of Oceanography La Jolla CA92093 United States

Radio-based localization approaches that make use of reflections in the propagation environment to improve the accuracy and robustness of location estimates have a variety of potential applications in future wireless communication networks. Multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy. © 1991-2012 IEEE.

关键词： Mapping

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Geographic Routing Protocol with Data Aggregation in Vehicular Ad-hoc Networks (VANETs) 11

Geographic Routing Protocol with Data Aggregation in Vehicul...

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11th International Conference on Information Systems and Advanced Technologies, ICISAT 2021

作者： Houacine, Abdelkrim Guezouri, Mustapha University of Saida Dr. Moulay Tahar Faculty of Technology Department of Computer Science Saida Algeria Faculty of Electrical Engineering University of Science and Technology Signal Image Laboratory Department of Electronics Oran Algeria

ISBN: (纸本)9781665478243

Unlike MANET (Mobil Ad Hoc Network), VANETs (Vehicular Ad Hoc Networks) are characterized by an unlimited number of vehicles can be equal to millions of vehicles and a high mobility. The objective of developing VANETs is to improve ITS (Intelligent Transportation Systems) in terms of road safety and efficiency in the transport field. Traditional routing protocols (packet based) used for MANETs (Mobile Ad hoc Networks) does not satisfy the routing requirements in VANETs due to high speed of vehicles. In this work we propose a routing protocol with data aggregation for VANETs, where we invoke the based point geographic routing and data aggregation. The results of our simulation with SUMO and NS-3 show that our approach can help to reduce network overload by aggregating road traffic reports without maintaining a hierarchical structure. © 2021 IEEE.

关键词： Vehicular ad hoc networks

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