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arXiv 2022年

作者： Ünal, Ali Burak Pfeifer, Nico Akgün, Mete Medical Data Privacy and Privacy-preserving ML Dept. of Computer Science University of Tuebingen Tübingen Germany Methods in Medical Informatics Dept. of Computer Science University of Tuebingen Tübingen Germany

Since machine learning algorithms have proven their success in many different applications, there is also a big interest in privacy preserving machine learning methods for building models on sensitive data. Moreover, the increase in the number of data sources and the high computational power required by those algorithms force individuals to outsource the training and/or the inference of a machine learning model to the clouds providing such services. To address this, we propose a secure three party computation framework, CECILIA, offering privacy preserving building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix. We use CECILIA to realize the private inference on pre-trained recurrent kernel networks, which require more complex operations than most other deep neural networks, on the structural classification of proteins as the first study ever accomplishing the privacy preserving inference on recurrent kernel networks. In addition to the successful private computation of basic building blocks, the results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, they also show that we compute the exact inverse square root of a secret Gram matrix up to a certain privacy level, which has not been addressed in the literature at all. We also analyze the scalability of CECILIA to various settings on a synthetic dataset. The framework shows a great promise to make other machine learning algorithms as well as further computations privatel

关键词： Adversarial machine learning

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GPU-accelerated PostgreSQL for Scalable Management and Processing of Irregular Time-Series Data using SPI

GPU-accelerated PostgreSQL for Scalable Management and Proce...

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IEEE International Conference on Big Data

作者： Jamal Saied-Walker Pallavi Gupta Ruhan Yi Noah Marchal Marjorie Skubic Grant J. Scott Dept. of EE & Computer Science University of Missouri Columbia Missouri Institute for Data Science & Informatics University of Missouri Columbia Missouri Center to Stream Healthcare in Place University of Missouri Columbia Missouri

As the demand for real-time signal processing increases in various fields, such as healthcare, artificial intelligence, machine learning, and scientific research, there is a need for more efficient methods to analyze large amounts of data. To address this challenge and explore the opportunities to accelerate different signal processing algorithms, this paper proposes the integration of graphics processing units (GPUs) with database management systems (DBMS) using the PostgreSQL server programming interface (SPI). The performance of the proposed method is evaluated by comparing central processing unit (CPU) and GPU approaches for feature extraction using a data processing pipeline for heart rate estimation from hydraulic bed sensor data. Furthermore, the paper analyzes timing metrics, usability, adaptability, and discusses precision differences between CPU and GPU code by performing different thread and block configurations.

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Transforming Energy Efficiency in IoMT: Advanced Modulation Strategies for Sustainable Wireless Medical Sensor Networks

Transforming Energy Efficiency in IoMT: Advanced Modulation ...

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Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： Ayoub Hadj Sadek Salma Rattal Gunjan Varshney El Miloud Ar-Reyouchi Kamal Ghoumid Dept. of Electronics Informatics and Telecommunications Mohammed I University ENSAO Oujda Morocco Ecole Marocaine des Sciences de l’Ingénieur (EMSI) Casablanca Morocco Dept. of Robotics and Artificial Intelligence JSS University Noida Noida India Dept. of Telecommunication and Computer Science Abdelmalek Essaâdi University Tetouan Morocco

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

The investigation focuses on advanced modulation strategies to optimize energy efficiency in Wireless Medical Sensor Networks (WMSNs) within the Internet of Medical Things (IoMT). The study addresses the balance between energy consumption and spectral efficiency, specifically targeting challenges associated with WMSN deployment in critical healthcare applications. The analysis rigorously evaluates energy consumption across various distances and modulation schemes, particularly in the presence of Additive White Gaussian Noise (AWGN) channels. Modulation techniques are categorized into linear and nonlinear types, allowing for a comprehensive examination of each class’s energy efficiency. The objective is to minimize energy usage while ensuring reliable and secure communication in remote healthcare systems through the optimal selection of modulation schemes and transmission parameters. The results offer significant contributions to sustainable healthcare technology, enhancing the operational lifespan of medical sensors without compromising data transmission quality.

关键词： Wireless communication Energy consumption Wireless sensor networks Power demand Spectral efficiency Machine learning Energy efficiency Reliability Communication system security Security

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Toward EEG-Based Brain State Recognition for Personalized Neuromodulation

Toward EEG-Based Brain State Recognition for Personalized Ne...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Yu-Cheng Chang Pin-Hsuan Chao Sin-Horng Chen Chun-Shu Wei Dept. of Computer Science Inst. of Biomedical Engineering (BME) NYCU Inst. of BME NYCU Dept. of Electronics and Electrical Engineering

ISBN: (纸本)9781665465007

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications.

关键词： Protocols Transcranial magnetic stimulation Multicore processing Firing Phase estimation Medical treatment Depression

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

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arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model

arXiv

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arXiv 2022年

作者： Hu, Dewei Tao, Yuankai K. Oguz, Ipek Vanderbilt University Dept. of Electrical and Computer Engineering United States Vanderbilt University Dept. of Biomedical Engineering United States Vanderbilt University Dept. of Computer Science NashvilleTN United States

Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans. Then the reverse process of diffusion, modeled by a Markov chain, provides an adjustable level of denoising. Our experiment results demonstrate that our method can significantly improve the image quality with a simple working pipeline and a small amount of training data. The implementation is available at https://***/DeweiHu/OCT_DDPM. © 2022, CC BY.

关键词： Optical tomography

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SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation

arXiv

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arXiv 2024年

作者： Yu, Jieming Wang, An Dong, Wenzhen Xu, Mengya Islam, Mobarakol Wang, Jie Bai, Long Ren, Hongliang Hong Kong Shenzhen Research Institute CUHK Shenzhen China Dept. of Computer Science and Engineering CUHK Hong Kong Dept. of Medical Physics and Biomedical Engineering Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom

The recent Segment Anything Model (SAM) 2 has demonstrated remarkable foundational competence in semantic segmentation, with its memory mechanism and mask decoder further addressing challenges in video tracking and object occlusion, thereby achieving superior results in interactive segmentation for both images and videos. Building upon our previous empirical studies, we further explore the zero-shot segmentation performance of SAM 2 in robot-assisted surgery based on prompts, alongside its robustness against real-world corruption. For static images, we employ two forms of prompts: 1-point and bounding box, while for video sequences, the 1-point prompt is applied to the initial frame. Through extensive experimentation on the MICCAI EndoVis 2017 and EndoVis 2018 benchmarks, SAM 2, when utilizing bounding box prompts, outperforms state-of-the-art (SOTA) methods in comparative evaluations. The results with point prompts also exhibit a substantial enhancement over SAM’s capabilities, nearing or even surpassing existing unprompted SOTA methodologies. Besides, SAM 2 demonstrates improved inference speed and less performance degradation against various image corruption. Although slightly unsatisfactory results remain in specific edges or regions, SAM 2’s robust adaptability to 1-point prompts underscores its potential for downstream surgical tasks with limited prompt requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Visual Interpretability of Deep Learning Models in Glaucoma Detection Using Color Fundus Images

Visual Interpretability of Deep Learning Models in Glaucoma ...

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2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： Lu, Da-Wen Hsu, Wei-Wen Huang, Yu-Chuan Lee, Lung-Chi Guo, Jing-Ming Hsiao, Yu-Ting Lin, I-Hung Chang, Yao-Chung Ko, Mei-Lan National Defense Medical Center Tri-Service General Hospital Dept. of Ophthalmology Taipei Taiwan National Taitung University Dept. of Computer Science and Information Engineering Taitung Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan National Taiwan University of Science and Technology Dept. of Electrical Engineering Taipei Taiwan Taipei Medical University Hospital Dept. of Ophthalmology Taipei Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital Dept. of Ophthalmology Hsinchu Taiwan National Tsing Hua University Dept. of Biomedical Engineering and Environmental Science Hsinchu Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

With the advancement of deep learning in many computer vision applications, the computer-aided diagnosis (CAD) systems were developed using the deep learning approaches to assist physicians in clinical diagnosis as a second opinion. Many studies have reported plausible performance of the deep learning models on glaucoma detection simply based on fundus images. However, the mechanism of the deep learning models in detecting glaucoma using color fundus is still not clear. Therefore, the deep features extracted by the deep learning models were discussed in this study to provide the visual interpretability for the frameworks of deep convolutional neural network (DCNN) in glaucoma assessment. In our experiments, 986 fundus photographs were collected from National Taiwan University Hospital Hsin-Chu Branch, categorized into two groups: the experimental group of 512 glaucomatous cases with ganglion cell complex (GCC) impairment and the control group of 474 non-glaucomatous cases with normal GCC thickness. The experimental results show the deep learning models mainly focus on the areas of optic nerve head (ONH) for the diagnosis of glaucoma, which is accordant to the clinical rules in glaucoma assessment. Surprisingly, the DCNN models can still achieved high prediction accuracy in detecting the glaucomatous cases with the cropped images of macular areas only. Several cases show the model's focus areas correspond to the region with GCC impairment. The results imply the deep learning models can detect the morphologically detailed alterations in fundus photographs, which may be beyond experts' visualization. © 2022 IEEE.

关键词： Eye protection

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Lossy Source Coding of Binary Memoryless Source by Trained LDGM Encoder

Lossy Source Coding of Binary Memoryless Source by Trained L...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Kazuki Ogawa Hiromu Takata Motohiko Isaka Dept. of Informatics Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan Dept. of Computer Science School of Engineering Kwansei Gakuin University Sanda Japan

ISBN: (数字)9784885523410

ISBN: (纸本)9781665499071

Use of low-density generator matrix codes as codebook and encoding with message-passing algorithm is an efficient approach for lossy coding of the i.i.d. binary source. We propose lossy source encoder which is trained on the associated unfolded Tanner graph for improving the rate-distortion performance and/or reducing computational cost.

关键词： Codes Source coding Rate-distortion Generators Computational efficiency

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