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

作者： Elisabeth Gabler Michael Nissen Thomas R. Altstidl Adriana Titzmann Kai Packhäuser Andreas Maier Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany Department of Computer Science Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nurnberg (FAU) Erlangen Germany

Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and *** relevance— To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.

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Retraction Note: Efficient user authentication protocol for distributed multimedia mobile cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 275-275页

作者： Vivekanandan, Manojkumar Sastry, V. N. Srinivasulu Reddy, U. Center for Mobile Banking (CMB) Institute for Development and Research in Banking Technology (IDRBT) Hyderabad India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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Computers in Cardiology (CinC)

作者： Hui Lu Julia Beatriz Yip Tobias Steigleder Stefan Grießhammer Maria Heckel Naga Venkata Sai Jitin Jami Bjoern Eskofier Christoph Ostgathe Alexander Koelpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the socio-demographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectively.

关键词： Heart Training Frequency-domain analysis Feature extraction data models Recording Convolutional neural networks

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Deep learning Based Prediction of Sun-Induced Fluorescence from Hyplant Imagery

Deep Learning Based Prediction of Sun-Induced Fluorescence f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jim Buffat Miguel Pato Kevin Alonso Stefan Auer Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Uwe Rascher Hanno Scharr Forschungszentrum Jülich GmbH Institute of Bio- and Geosciences IBG-2: Plant Sciences Jülich Germany German Aerospace Center (DLR) Earth Observation Center Remote Sensing Technology Institute Oberpfaffenhofen Germany RHEA Group c/o European Space Agency (ESA) Frascati Italy Forschungszentrum Jülich GmbH Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Jülich Germany

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

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Fast machine learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant

Fast Machine Learning Simulator of At-Sensor Radiances for S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Miguel Pato Kevin Alonso Stefan Auer Jim Buffat Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Uwe Rascher Hanno Scharr Earth Observation Center Remote Sensing Technology Institute German Aerospace Center (DLR) Germany Largo Galileo Galilei RHEA Group c/o European Space Agency (ESA) Frascati Italy Institute of Bio- and Geosciences IBG-2: Plant Sciences Forschungszentrum Jülich GmbH Julich Germany Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O 2 -A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O 2 -A band with DESIS and HyPlant.

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Development of water flood model for oil production enhancement

Development of water flood model for oil production enhancem...

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2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

作者： Tsvaki, Jetina J. Tailakov, Dmitry O. Pavlovskiy, Evgeniy N. Novosibirsk State University Dept. of Mathematics and Mechanics Novosibirsk Russia Kutateladze Institute of Thermophysics Sb Ras Laboratory for Fundamentals of Energy Technologies Novosibirsk Russia Novosibirsk State University Stream Data Analytics and Machine Learning Laboratory Novosibirsk Russia

ISBN: (纸本)9780738131115

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity. © 2020 IEEE.

关键词： Floods

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Monitoring medication optimization in patients with Parkinson’s disease

Monitoring medication optimization in patients with Parkinso...

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

作者： Hamid Moradi Julius Hannink Sabine Stallforth Till Gladow Stefan Ringbauer Martin Mayr Jürgen Winkler Jochen Klucken Bjoern M. Eskofier Department of Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Laboratory (MaD Lab) Friedrich Alexander University Erlangen Nuremberg Erlangen-Nuremberg Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular-Neurology University Hospital Erlangen Erlangen Germany Medical Valley Digital Health Application Center Bamberg Germany NeuroSys GmbH Ulm Germany Luxembourg Institute of Health Centre Hospitalier du Luxembourg University of Luxembourg

Medication optimization is a common component of the treatment strategy in patients with Parkinson’s disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient’s onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient’s need. Additionally, they help to observe the patient’s response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson’s disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal *** relevance— Our proposed gait analysis method in this study provides objective, detailed, and punctual information to physicians. Revealing clinically relevant time points related to the patient’s need for medical adaption alleviates therapy optimization for physicians and reduces the duration of suboptimal treatment for patients. As the home-monitoring system acts remotely, embedding it in the medical care pathways could improve patients’ quality of life.

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Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text data

Exploring the Capabilities of a Language Model-Only Approach...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Misha Sadeghi Bernhard Egger Reza Agahi Robert Richer Klara Capito Lydia Helene Rupp Lena Schindler-Gmelch Matthias Berking Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany Department of Computer Science Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Syenah GMBH Eschborn Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.

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Computational Approach for Respiratory Pressure Parameters in Neonatal Ventilation

Computational Approach for Respiratory Pressure Parameters i...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Daria Lipchak Aleksey Chupov Anton Dolganov Aleksei Zhdanov Medical Devices R&D Bureau Joint Stock Company “Production Association “Urals Optical & Mechanical Plant” named after Mr. E. S. Yalamov” Yekaterinburg Russia Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University named after the first President of Russia B.N. Yeltsin Yekaterinburg Russia Machine Learning and Data Analytics Lab University of Erlangen-Nuremberg Erlangen Germany

In the context of mechanical ventilation, the accurate calculation of secondary pressure parameters is essential for clinicians to effectively assess a patient's clinical condition. In the case of neonatal resuscitation with the T-piece, both the inspiratory and expiratory triggers are manually operated. The device provides a constant flow at the initiation of inspiration and reduces the flow to limit output pressure according to the set peak inspiratory pressure. However, without an algorithmic classification of the inspiration and expiration pressure, data analysis may only be performed postfactum. To address the need for the separate trend analysis of PIP and PEEP in non-invasive mechanical ventilation with a T-piece, there is a requirement to develop and implement an accurate algorithm for respiratory pressure parameters calculation. In this study, we review the current state-of-the-art methods for the calculation of respiratory pressure parameters for non-invasive mechanical ventilation with a T-piece. We then present the development and implementation of an algorithm for respiratory pressure parameters calculation and discuss the results of the analysis. This algorithm is designed to provide accurate and reliable measurements of PIP and PEEP in neonatal resuscitation with the T-piece, allowing clinicians to more effectively monitor and assess a patient's clinical condition during mechanical ventilation. The results demonstrate the effectiveness of the algorithm in accurately calculating PIP and PEEP values, providing valuable insights into the patient's respiratory mechanics and helping to inform clinical decision-making.

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DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

arXiv

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arXiv 2023年

作者： Ding, Lisang Jin, Kexin Ying, Bicheng Yuan, Kun Yin, Wotao Department of Mathematics University of California Los AngelesCA United States Department of Mathematics Princeton University PrincetonNJ United States Google Inc. Los AngelesCA United States Center for Machine Learning Research Peking University Beijing China AI for Science Institute Beijing China National Engineering Labratory for Big Data Analytics and Applications Beijing China Decision Intelligence Lab. Alibaba US BellevueWA United States

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose Decentralized SGD with Communication-optimal Exact Consensus Algorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an Õ(n3) transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD’s convergence analysis. Numerical experiments show the efficiency of DSGD-CECA. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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