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arXiv 2023年

作者： Chen, Zhengyang Han, Bing Wang, Shuai Qian, Yanmin MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University China Shenzhen Research Institute of Big Data Shenzhen China

This paper proposes a novel Attention-based Encoder-Decoder network for End-to-End Neural speaker Diarization (AED-EEND). In AED-EEND system, we incorporate the target speaker enrollment information used in target speaker voice activity detection (TS-VAD) to calculate the attractor, which can mitigate the speaker permutation problem and facilitate easier model convergence. In the training process, we propose a teacher-forcing strategy to obtain the enrollment information using the ground-truth label. Furthermore, we propose three heuristic decoding methods to identify the enrollment area for each speaker during the evaluation process. Additionally, we enhance the attractor calculation network LSTM used in the end-to-end encoder-decoder based attractor calculation (EEND-EDA) system by incorporating an attention-based model. By utilizing such an attention-based attractor decoder, our proposed AED-EEND system outperforms both the EEND-EDA and TS-VAD systems with only 0.5s of enrollment data. © 2023, CC BY-NC-SA.

关键词： Heuristic methods

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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A joint method of coherence factor and nonlinear beamforming for synthetic aperture imaging with a ring array

A joint method of coherence factor and nonlinear beamforming...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Zhengfeng Lan Chao Rong Changshan Han Xiaolei Qu Jingsong Li Hongxiang Lin Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Ultrasound computed tomography (USCT) with a ring array is an emerging diagnostic method for breast cancer. In the literature, synthetic aperture (SA) imaging has employed the delay-and-sum (DAS) beamforming technique for ring-array USCT to obtain isotropic resolution reflection images. However, the images obtained by the conventional DAS beamformer suffer from off-axis clutter and low resolution due to inhomogeneity of the medium and phase distortion. To address these issues, researchers have developed adaptive beamforming methods, such as coherence factor (CF) and convolutional beamforming algorithm (COBA), that improve image quality. In this study, we propose a joint method that combines CF with short-lag COBA (SLCOBA). First, we estimate the average sound speed using CF to address tissue inhomogeneity. Based on the corrected sound speed map, SLCOBA effectively suppresses side lobes and enhances image quality. Numerical results show that the proposed method reduces clutter and noise, improving resolution performance. These findings suggest that the proposed method may be a practical option for breast imaging in inhomogeneous mediums in the future.

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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

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Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients 1

Structuring Clinical Notes of Italian ST-elevation Myocardia...

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1st Workshop on Patient-Oriented Language Processing, CL4Health 2024

作者： Torri, Vittorio Mazzucato, Sara Dalmiani, Stefano Paradossi, Umberto Passino, Claudio Moccia, Sara Micera, Silvestro Ieva, Francesca MOX - Modelling and Scientific Computing Lab Department of Mathematics Politecnico di Milano Italy Biorobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy Fondazione Toscana Gabriele Monasterio Italy Health Science Interdisciplinary Center Scuola Superiore Sant’Anna Italy Bertarelli Foundation Chair in Translational Neural Engineering Center for Neuroprosthetics Institute of Bioengineering Ecole Polytechnique Federale de Lausanne Switzerland HDS - Health Data Science Centre Human Technopole Italy

ISBN: (纸本)9782493814258

In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure anamneses, providing patients with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from EHRs of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score > 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Recurrent neural networks

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Mixed Attention Densely Residual Network for Single Image Super-Resolution

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Computer Systems science & engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Combining Self-Supervised Learning and Adversarial Training Based Domain Adaptation for Speaker Verification

Combining Self-Supervised Learning and Adversarial Training ...

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International Symposium on Chinese Spoken Language Processing

作者： Zhengyang Chen Shuai Wang Bing Han Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai Shenzhen Research Institute of Big Data Shenzhen Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Adapting an existing well-trained system to a new domain using only unlabeled data is a highly sought-after yet challenging task for speaker verification in real-world scenarios. In this paper, we study two different domain adaptation methods, the adversarial domain adaptation (ADA) and the self-supervised learning-based domain adaptation (SSDA). To facilitate the deployment of unsupervised adaptation methods in applications, we conduct a detailed analysis of the characteristics of both the ADA and SSDA adaptation strategies. Our findings indicate that the SSDA strategy's performance is highly influenced by the amount of target domain data, whereas the ADA strategy is relatively insensitive to data quantity. Furthermore, augmenting target domain data enhances SSDA system performance but diminishes ADA performance. To further enhance system performance, we explore the complementarity between ADA and SSDA. Our results demonstrate that ADA and SSDA complement each other. When both strategies are applied jointly, the best system achieves over 20.0% relative Equal Error Rate (EER) improvement on the Cnceleb evaluation set and over 35.0% relative average EER improvement on the SRE16 Cantonese and Tagalog evaluation set under domain mismatched conditions.

关键词： Training Error analysis System performance Self-supervised learning Speech processing

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Location Optimization and Resource Allocation of IRS in a Multi-User Indoor mmWave VR Network

Location Optimization and Resource Allocation of IRS in a Mu...

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IEEE Conference on Wireless Communications and Networking

作者： Jalal Jalali Maria Bustamante Madrid Filip Lemic Hina Tabassum Jakob Struye Jeroen Famaey Xavier Costa Perez Internet and Data Lab University of Antwerp - imec Belgium AI-Driven Systems Lab i2Cat Foundation Spain Electrical Engineering and Computer Science Department York University Canada NEC Labs Europe GmbH Germany ICREA Spain

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

关键词： Wireless communication Surface waves Array signal processing Virtual reality Throughput Trajectory Resource management

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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