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Jose Elias Yauri; M. Lagos; H. Vega-Huerta; P. de-la-Cruz; G.L.E Maquen-Niño; E. Condor-Tinoco |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings |
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Journal Article |
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Year |
2023 |
Publication |
International Journal of Advanced Computer Science and Applications |
Abbreviated Journal |
IJACSA |
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14 |
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5 |
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1067-1074 |
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Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention |
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According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches. |
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3856 |
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Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images |
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Conference Article |
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2023 |
Publication |
7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 |
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This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level. |
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DIPMAI |
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Admin @ si @ TRB2023 |
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3926 |
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Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
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Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
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Conference Article |
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2023 |
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37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery |
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Munich; Germany; June 2023 |
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CARS |
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Admin @ si @ TGR2023a |
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3950 |
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Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
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Title |
Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
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Conference Article |
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Year |
2023 |
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SEPAR |
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Granada; Spain; June 2023 |
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Admin @ si @ BGS2023 |
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3951 |
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Debora Gil; Guillermo Torres; Carles Sanchez |
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Title |
Transforming radiomic features into radiological words |
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Conference Article |
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2023 |
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IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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Thesis ![sorted by Thesis field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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ISBI |
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IAM |
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Admin @ si @ GTS2023 |
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3952 |
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Author |
Pau Cano; Debora Gil; Eva Musulen |
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Title |
Towards automatic detection of helicobacter pylori in histological samples of gastric tissue |
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Conference Article |
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2023 |
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IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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ISBI |
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IAM |
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no |
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Admin @ si @ CGM2023 |
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3953 |
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Author |
Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
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Title |
A radiomic biopsy for virtual histology of pulmonary nodules |
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Conference Article |
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2023 |
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IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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IAM |
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Admin @ si @ TGR2023b |
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3954 |
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Author |
Eric Amiel |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Visualisation de vaisseaux sanguins |
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Report |
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2005 |
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Rapport de Stage |
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Université Paul Sabatier Toulouse III |
Thesis ![sorted by Thesis field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Bachelor's thesis |
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Université Paul Sabatier Toulouse III |
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Toulouse |
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Enric Marti |
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French |
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French |
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IUP Systèmes Intelligents |
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IAM @ iam @ Ami2005 |
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1690 |
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Author |
Albert Andaluz |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction |
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Report |
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2009 |
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CVC Technical Report |
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142 |
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Active Contour Models; Snakes; Active Shape Models; Deformable Templates; Left Ventricle Segmentation; Generalized Orthogonal Procrustes Analysis; Harmonic Phase Flow; Principal Component Analysis; Tagged Magnetic Resonance |
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The Diagnosis of Left Ventricle (LV) pathologies is related to regional wall motion analysis. Health indicator scores such as the rotation and the torsion are useful for the diagnose of the Left Ventricle (LV) function. However, this requires proper identification of LV segments. On one hand, manual segmentation is robust, but it is slow and requires medical expertise. On the other hand, the tag pattern in Tagged Magnetic Resonance (TMR) sequences is a problem for the automatic segmentation of the LV boundaries. Consequently, we propose a method based in the classical formulation of parametric Snakes, combined with Active Shape models. Our semantic definition of the LV is tagged tissue that experiences motion in the systolic cycle. This defines two energy potentials for the Snake convergence. Additionally, the mean shape corrects excessive deviation from the anatomical shape. We have validated our approach in 15 healthy volunteers and two short axis cuts. In this way, we have compared the automatic segmentations to manual shapes outlined by medical experts. Also, we have explored the accuracy of clinical scores computed using automatic contours. The results show minor divergence in the approximation and the manual segmentations as well as robust computation of clinical scores in all cases. From this we conclude that the proposed method is a promising support tool for clinical analysis. |
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Thesis ![sorted by Thesis field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Master's thesis |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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IAM @ iam @ And2009 |
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1667 |
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Author |
Jaume Garcia |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Generalized Active Shape Models Applied to Cardiac Function Analysis |
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Report |
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2004 |
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CVC Technical Report |
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78 |
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Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography. |
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Abstract |
Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown. |
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CVC (UAB) |
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Master's thesis |
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IAM; |
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IAM @ iam @ Gar2004 |
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1513 |
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