Machine Learning for Predictive Modelling of Ambulance Calls

Yu, Miao; Kollias, Dimitrios; Wingate, James; Siriwardena, Aloysius Niroshan; Kollias, Stefanos

doi:10.20944/preprints202101.0066.v1

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…The presented approach is based on a CNN-RNN architecture that performs 3-D CT scan analysis. The method follows our previous work [4,6,5,12] on developing deep neural architectures for predicting COVID-19, as well as neurodegenerative and other [7,5,13,16] diseases and abnormal situations [11,1,14,9].…”

Section: Related Workmentioning

confidence: 99%

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

Kollias¹,

Arsenos²,

Kollias³

2022

Preprint

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This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.

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Section: Related Workmentioning

confidence: 99%

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

Kollias¹,

Arsenos²,

Kollias³

2022

Preprint

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“…The '68 landmarks' are concatenated with the features of the last 'pool' layer and passed as input to the 'fc' layer. networks, while Decision-level fusion is based on computing a weighted average of the predictions [30] provided by the different networks. On the one hand side, Model-level fusion takes advantage of the mutual information in the data.…”

Section: ) Ensemble Methodologymentioning

confidence: 99%

Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework

Kollias,

Zafeiriou

2021

Preprint

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Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis and recognition in-the-wild. Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes of deep neural networks trained with these databases. The first class refers to uni-task affect recognition, focusing on prediction of the valence and arousal dimensional variables. The second class refers to estimation of all main behavior tasks, i.e. valence-arousal prediction; categorical emotion classification in seven basic facial expressions; facial Action Unit detection. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition over all existing in-the-wild databases. Large experimental studies illustrate the achieved performance improvement over the existing state-of-the-art in affect recognition.

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“…The presented approach is based on a CNN-RNN architecture that performs 3-D CT scan analysis. The method follows our previous work [4,6,5,11] on developing deep neural architectures for predicting COVID-19, as well as neurodegenerative and other [7,5,12,14] diseases and medical situations.…”

Section: Related Workmentioning

confidence: 99%

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Kollias¹,

Arsenos²,

Soukissian³

et al. 2021

Preprint

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Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 based on CT scans.In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in training, validation and test datasets. The former two datasets can be used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. We also present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.

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Machine Learning for Predictive Modelling of Ambulance Calls

Cited by 6 publications

References 20 publications

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

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