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Model-Based and Data-Driven Strategies in Medical Image Computing [article]

Daniel Rueckert, Julia A. Schnabel
2019 arXiv   pre-print
A challenge for these approaches is the modelling of the underlying processes (e.g. the physics of image acquisition or the patho-physiology of a disease) with appropriate levels of detail and realism.  ...  Model-based approaches for image reconstruction, analysis and interpretation have made significant progress over the last decades.  ...  P023509/1, EP/N026993/1), the Wellcome Trust/EPSRC Centre of Medical Engineering (NS/A000049/1), the Wellcome Trust/EPSRC IEH Award (NS/A000025/1) and the Innovate UK London Medical Imaging and AI Centre for  ... 
arXiv:1909.10391v3 fatcat:m3kpdg2sf5ai7cfjokhy7so2iy

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Muhammad Fahim, Alberto Sillitti
2019 IEEE Access  
In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas.  ...  He has served as a member for the program committee of several international conferences, as a Program Chair  ...  A summary of machine learning methods is presented in Table 15 . 3) DEEP LEARNING MODELS We also found limited deep learning models for anomaly detection in the considered domains.  ... 
doi:10.1109/access.2019.2921912 fatcat:k7pmdn6ruzevrpyibo7dmqh3ee

Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome [article]

Elisa Chotzoglou, Thomas Day, Jeremy Tan, Jacqueline Matthew, David Lloyd, Reza Razavi, John Simpson, Bernhard Kainz
2021 arXiv   pre-print
In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of  ...  We evaluate a number of known anomaly detection frameworks together with a model architecture based on the α-GAN network and find evidence that the proposed model performs significantly better than the  ...  Finally, in (Pidhorskyi et al., 2018) a probabilistic framework is proposed which is based on a model similar to α-GAN.  ... 
arXiv:2012.03679v2 fatcat:2ebihuw6wrgs5l33aedfci65eu

From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review

Francesco Galati, Sébastien Ourselin, Maria A. Zuluaga
2022 Applied Sciences  
of DL segmentation models, which can be critical if a model was to be deployed into clinical practice.  ...  Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance.  ...  [93] extend the well-established U-net architecture [94] through the formulation of a probabilistic framework, which allows the embedding of a cardiac shape prior, in the form of a 3D volume encoding  ... 
doi:10.3390/app12083936 fatcat:p4rg6p27dbgr5ju44wireqwjaa

Evaluation of Spatial-Temporal Anomalies in the Analysis of Human Movement

Rui Varandas, Duarte Folgado, Hugo Gamboa
2019 Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies  
It is proposed a framework capable of detecting anomalies in generic repetitive time series, thus being adequate to handle Human motion from industrial scenarios.  ...  The proposed framework consists of (1) a new unsupervised segmentation algorithm; (2) feature extraction, selection and dimensionality reduction; (3) unsupervised classification based on DBSCAN used to  ...  Concretely, the objectives are: (1) explore the viability of unsupervised anomaly detection on human motion data acquired with resource to inertial sensors; (2) develop a new unsupervised anomaly detection  ... 
doi:10.5220/0007386701630170 dblp:conf/biostec/VarandasFG19 fatcat:abgbfga4cbgljar2ulrahkkjxi

2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25

2021 IEEE journal of biomedical and health informatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Estimating Reference Bony Shape Models for Orthognathic Surgical Plan-Luo, X., +, JBHI Nov. 2021 4098-4109 ning Using 3D Point-Cloud Deep Learning.  ...  Kumar, A., +, JBHI March 2021 701-710 A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos.  ... 
doi:10.1109/jbhi.2022.3140980 fatcat:ufig7b54gfftnj3mocspoqbzq4

Paper titles

2020 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)  
Scene Detection Based on Anomaly Detection using Long Short-Term Memory for Baseball Highlight Generation Improved Normalized Probabilistic Minimum Summation Algorithm for LDPC decoding Improving Fuzzy  ...  Unit Design for a Wearable ECG Application A Practical Exercise System Using Virtual Machines for Learning Cross-Site Scripting Countermeasures A Probability-Based Analytical Model Based on Deep Learning  ... 
doi:10.1109/icce-taiwan49838.2020.9258179 fatcat:2eheaztzhncixhbvp7nrbzml4m

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Monica Micucci, Antonio Iula
2022 Electronics  
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems.  ...  The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation  ...  a full and reliable assessment of cardiac functionality improving diagnosis accuracy [139, 140] .  ... 
doi:10.3390/electronics11111800 fatcat:htw3q5kednhkbndgk7vw3tbvya

State-of-the-art review on deep learning in medical imaging

Jasjit S Suri
2019 Frontiers in Bioscience  
Review on deep learning in medical imaging 393  ...  We would like to thank the publishers for approving usage of images in our paper. We would like to thank MediaLab Asia, DEITY for their encouragement and support.  ...  In this respect, given the cardiac phase and contour, the shape model can be described probabilistically as: p I k c p k c I p I | , θ ( ), non-rigid segmentation p c I | ,k , θ ( ) and prior distribution  ... 
doi:10.2741/4725 fatcat:lh5b3okh4jcq5aogjfowjfdaqy

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Unsupervised Classification in Multiple Sclerosis Transferable Model for Shape Optimization subject to Physical Constraints DAY 1 -Jan 12, 2021 Morsing, Hedegaard, Lukas; Sheikh- Omar, Omar Ali; Iosifidis  ...  Estimation from RGB Video: On-Line Camera Self- Calibration, Non-Rigid Shape and Motion DAY 2 -Jan 13, 2021 Heitzinger, Thomas; Kampel, Martin 2260 IPT: A Dataset for Identity Preserved Tracking  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Unconstrained Video Monitoring of Breathing Behavior and Application to Diagnosis of Sleep Apnea

Ching-Wei Wang, Andrew Hunter, Neil Gravill, Simon Matusiewicz
2014 IEEE Transactions on Biomedical Engineering  
We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online  ...  This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea.  ...  the cardiac and respiratory motion [18] , [19] .  ... 
doi:10.1109/tbme.2013.2280132 pmid:24001952 fatcat:g642asfmw5bevgjoiiihrshbk4

BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Sergio Márquez-Sánchez, Israel Campero-Jurado, Daniel Robles-Camarillo, Sara Rodríguez, Juan M. Corchado-Rodríguez
2021 Sensors  
That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised.  ...  In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly.  ...  Supported by the project "Computación cuántica, virtualización de red, edge computing y registro distribuido para la inteligencia artificial del futuro", Reference: CCTT3/20/SA/0001, financed by Institute for  ... 
doi:10.3390/s21103372 pmid:34066186 fatcat:wyl5bskorfc6jiwq5luecrkbwu

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis [article]

Maria Chiara Fiorentino and Francesca Pia Villani and Mariachiara Di Cosmo and Emanuele Frontoni and Sara Moccia
2022 arXiv   pre-print
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images.  ...  This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working  ...  A mAP of 0.70 is achieved in detecting cardiac substructures along with a mean AUC of 0.83 in the assessing of cardiac structural abnormalities detection.  ... 
arXiv:2201.12260v1 fatcat:hewsv3i3vfbzjjt3sakunb2g2a

Deep Learning for Health Informatics

Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, Guang-Zhong Yang
2017 IEEE journal of biomedical and health informatics  
Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence  ...  This article presents a comprehensive up-todate review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique  ...  [54] developed a fully automated shape model segmentation mechanism for the analysis of cranial nerve systems.  ... 
doi:10.1109/jbhi.2016.2636665 pmid:28055930 fatcat:24hfhfasljhehb2phndoyu5rnm

Table of Contents

2020 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)  
Spatial Distance Model for Brownian Motion, pp. 969-972.  ...  : An Unsupervised Deep Learning Framework, pp. 600-604.  ... 
doi:10.1109/isbi45749.2020.9098467 fatcat:6kxbkb2s5bdc5cmjvxjhotccay
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