15 research outputs found
A Makefile for Developing Containerized LaTeX Technical Documents
We propose a Makefile for developing containerized technical
documents. The Makefile allows the author to execute the code that generates
variables, tables and figures (results), which are then used during the
compilation, to produce either the draft (fast) or full (slow) version
of the document. We also present various utilities that aid in automating the
results generation and improve the reproducibility of the document. We release
an open source repository of a template that uses the Makefile and demonstrate
its use by developing this paper.Comment: 3 pages, 3 figures, 1 tabl
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Signal2Image Modules in Deep Neural Networks for EEG Classification
Deep learning has revolutionized computer vision utilizing the increased
availability of big data and the power of parallel computational units such as
graphical processing units. The vast majority of deep learning research is
conducted using images as training data, however the biomedical domain is rich
in physiological signals that are used for diagnosis and prediction problems.
It is still an open research question how to best utilize signals to train deep
neural networks.
In this paper we define the term Signal2Image (S2Is) as trainable or
non-trainable prefix modules that convert signals, such as
Electroencephalography (EEG), to image-like representations making them
suitable for training image-based deep neural networks defined as `base
models'. We compare the accuracy and time performance of four S2Is (`signal as
image', spectrogram, one and two layer Convolutional Neural Networks (CNNs))
combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet)
along with the depth-wise and 1D variations of the latter. We also provide
empirical evidence that the one layer CNN S2I performs better in eleven out of
fifteen tested models than non-trainable S2Is for classifying EEG signals and
we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201
Sparsely Activated Networks
Previous literature on unsupervised learning focused on designing structural
priors with the aim of learning meaningful features. However, this was done
without considering the description length of the learned representations which
is a direct and unbiased measure of the model complexity. In this paper, first
we introduce the metric that evaluates unsupervised models based on
their reconstruction accuracy and the degree of compression of their internal
representations. We then present and define two activation functions (Identity,
ReLU) as base of reference and three sparse activation functions (top-k
absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize
the previously defined . We lastly present Sparsely Activated Networks
(SANs) that consist of kernels with shared weights that, during encoding, are
convolved with the input and then passed through a sparse activation function.
During decoding, the same weights are convolved with the sparse activation map
and subsequently the partial reconstructions from each weight are summed to
reconstruct the input. We compare SANs using the five previously defined
activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST,
FMNIST) and show that models that are selected using have small
description representation length and consist of interpretable kernels.Comment: 10 pages, 5 figures, 4 algorithms, 4 tables, submission to IEEE
Transactions on Neural Networks and Learning System
Sparsely Activated Networks: A new method for decomposing and compressing data
Recent literature on unsupervised learning focused on designing structural
priors with the aim of learning meaningful features, but without considering
the description length of the representations. In this thesis, first we
introduce the{\phi}metric that evaluates unsupervised models based on their
reconstruction accuracy and the degree of compression of their internal
representations. We then present and define two activation functions (Identity,
ReLU) as base of reference and three sparse activation functions (top-k
absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize
the previously defined metric . We lastly present Sparsely Activated
Networks (SANs) that consist of kernels with shared weights that, during
encoding, are convolved with the input and then passed through a sparse
activation function. During decoding, the same weights are convolved with the
sparse activation map and subsequently the partial reconstructions from each
weight are summed to reconstruct the input. We compare SANs using the five
previously defined activation functions on a variety of datasets (Physionet,
UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using
have small description representation length and consist of
interpretable kernels.Comment: PhD Thesis in Greek, 158 pages for the main text, 23 supplementary
pages for presentation, arXiv:1907.06592, arXiv:1904.13216, arXiv:1902.1112
Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans
Recently there has been an explosion in the use of Deep Learning (DL) methods
for medical image segmentation. However the field's reliability is hindered by
the lack of a common base of reference for accuracy/performance evaluation and
the fact that previous research uses different datasets for evaluation. In this
paper, an extensive comparison of DL models for lung and COVID-19 lesion
segmentation in Computerized Tomography (CT) scans is presented, which can also
be used as a benchmark for testing medical image segmentation models. Four DL
architectures (Unet, Linknet, FPN, PSPNet) are combined with 25 randomly
initialized and pretrained encoders (variations of VGG, DenseNet, ResNet,
ResNext, DPN, MobileNet, Xception, Inception-v4, EfficientNet), to construct
200 tested models. Three experimental setups are conducted for lung
segmentation, lesion segmentation and lesion segmentation using the original
lung masks. A public COVID-19 dataset with 100 CT scan images (80 for train, 20
for validation) is used for training/validation and a different public dataset
consisting of 829 images from 9 CT scan volumes for testing. Multiple findings
are provided including the best architecture-encoder models for each experiment
as well as mean Dice results for each experiment, architecture and encoder
independently. Finally, the upper bounds improvements when using lung masks as
a preprocessing step or when using pretrained models are quantified. The source
code and 600 pretrained models for the three experiments are provided, suitable
for fine-tuning in experimental setups without GPU capabilities.Comment: 10 pages, 8 figures, 2 table
3-D Registration on Carotid Artery imaging data: MRI for different timesteps
A common problem which is faced by the researchers when dealing with arterial
carotid imaging data is the registration of the geometrical structures between
different imaging modalities or different timesteps. The use of the "Patient
Position" DICOM field is not adequate to achieve accurate results due to the
fact that the carotid artery is a relatively small structure and even
imperceptible changes in patient position and/or direction make it difficult.
While there is a wide range of simple/advanced registration techniques in the
literature, there is a considerable number of studies which address the
geometrical structure of the carotid artery without using any registration
technique. On the other hand the existence of various registration techniques
prohibits an objective comparison of the results using different registration
techniques. In this paper we present a method for estimating the statistical
significance that the choice of the registration technique has on the carotid
geometry. One-Way Analysis of Variance(ANOVA) showed that the p-values were
<0.0001 for the distances of the lumen from the centerline for both right and
left carotids of the patient case that was studied.Comment: 4 pages, 4 figures, 1 table, preprint submitted to IEEE-EMBC 201
The holistic perspective of the INCISIVE Project: artificial intelligence in screening mammography
Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.This research received funding mainly from the European Union’s Horizon 2020 research and innovation program under grant agreement no 952179. It was also partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under contracts PID2019-107255GB and 2017-SGR-1414.Peer ReviewedArticle signat per 30 autors/es: Ivan Lazic (1), Ferran Agullo (2), Susanna Ausso (3), Bruno Alves (4), Caroline Barelle (4), Josep Ll. Berral (2), Paschalis Bizopoulos (5), Oana Bunduc (6), Ioanna Chouvarda (7), Didier Dominguez (3), Dimitrios Filos (7), Alberto Gutierrez-Torre (2), Iman Hesso (8), Nikša Jakovljević (1), Reem Kayyali (8), Magdalena Kogut-Czarkowska (9), Alexandra Kosvyra (7), Antonios Lalas (5) , Maria Lavdaniti (10,11), Tatjana Loncar-Turukalo (1),Sara Martinez-Alabart (3), Nassos Michas (4,12), Shereen Nabhani-Gebara (8), Andreas Raptopoulos (6), Yiannis Roussakis (13), Evangelia Stalika (7,11), Chrysostomos Symvoulidis (6,14), Olga Tsave (7), Konstantinos Votis (5) Andreas Charalambous (15) / (1) Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; (2) Barcelona Supercomputing Center, 08034 Barcelona, Spain; (3) Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain; (4) European Dynamics, 1466 Luxembourg, Luxembourg; (5) Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece; (6) Telesto IoT Solutions, London N7 7PX, UK: (7) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (8) Department of Pharmacy, Kingston University London, London KT1 2EE, UK; (9) Timelex BV/SRL, 1000 Brussels, Belgium; (10) Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece; (11) Hellenic Cancer Society, 11521 Athens, Greece; (12) European Dynamics, 15124 Athens, Greece; (13) German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus; (14) Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece; (15) Department of Nursing, Cyprus University of Technology, Limassol 3036, CyprusPostprint (published version
The holistic perspective of the INCISIVE project : artificial intelligence in screening mammography
Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions