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Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell |
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Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study |
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2021 |
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American Journal of Respiratory and Critical Care Medicine |
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IAM; 600.145 |
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Admin @ si @ BDS2021 |
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3591 |
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Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; B. Cardenas; G. Fonseka; E. Anton; Alvaro Pascual; Richard Frodsham; Zaida Sarrate |
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Time to match; when do homologous chromosomes become closer? |
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2022 |
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Chromosoma |
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CHRO |
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In most eukaryotes, pairing of homologous chromosomes is an essential feature of meiosis that ensures homologous recombination and segregation. However, when the pairing process begins, it is still under investigation. Contrasting data exists in Mus musculus, since both leptotene DSB-dependent and preleptotene DSB-independent mechanisms have been described. To unravel this contention, we examined homologous pairing in pre-meiotic and meiotic Mus musculus cells using a threedimensional fuorescence in situ hybridization-based protocol, which enables the analysis of the entire karyotype using DNA painting probes. Our data establishes in an unambiguously manner that 73.83% of homologous chromosomes are already paired at premeiotic stages (spermatogonia-early preleptotene spermatocytes). The percentage of paired homologous chromosomes increases to 84.60% at mid-preleptotene-zygotene stage, reaching 100% at pachytene stage. Importantly, our results demonstrate a high percentage of homologous pairing observed before the onset of meiosis; this pairing does not occur randomly, as the percentage was higher than that observed in somatic cells (19.47%) and between nonhomologous chromosomes (41.1%). Finally, we have also observed that premeiotic homologous pairing is asynchronous and independent of the chromosome size, GC content, or presence of NOR regions. |
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August, 2022 |
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IAM; 601.139; 600.145; 600.096 |
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Admin @ si @ SBG2022 |
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3719 |
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Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
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Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project |
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2023 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCARS |
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IAM |
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Admin @ si @ TGM2023 |
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3830 |
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Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil |
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Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images |
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2023 |
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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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IAM |
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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 |
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Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
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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 |
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Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
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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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Transforming radiomic features into radiological words |
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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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Admin @ si @ GTS2023 |
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3952 |
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Pau Cano; Debora Gil; Eva Musulen |
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Towards automatic detection of helicobacter pylori in histological samples of gastric tissue |
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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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Admin @ si @ CGM2023 |
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3953 |
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Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez |
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A radiomic biopsy for virtual histology of pulmonary nodules |
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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 |
Jose Elias Yauri |
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Deep Learning Based Data Fusion Approaches for the Assessment of Cognitive States on EEG Signals |
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2023 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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For millennia, the study of the couple brain-mind has fascinated the humanity in order to understand the complex nature of cognitive states. A cognitive state is the state of the mind at a specific time and involves cognition activities to acquire and process information for making a decision, solving a problem, or achieving a goal.
While normal cognitive states assist in the successful accomplishment of tasks; on the contrary, abnormal states of the mind can lead to task failures due to a reduced cognition capability. In this thesis, we focus on the assessment of cognitive states by means of the analysis of ElectroEncephaloGrams (EEG) signals using deep learning methods. EEG records the electrical activity of the brain using a set of electrodes placed on the scalp that output a set of spatiotemporal signals that are expected to be correlated to a specific mental process.
From the point of view of artificial intelligence, any method for the assessment of cognitive states using EEG signals as input should face several challenges. On the one hand, one should determine which is the most suitable approach for the optimal combination of the multiple signals recorded by EEG electrodes. On the other hand, one should have a protocol for the collection of good quality unambiguous annotated data, and an experimental design for the assessment of the generalization and transfer of models. In order to tackle them, first, we propose several convolutional neural architectures to perform data fusion of the signals recorded by EEG electrodes, at raw signal and feature levels. Four channel fusion methods, easy to incorporate into any neural network architecture, are proposed and assessed. Second, we present a method to create an unambiguous dataset for the prediction of cognitive mental workload using serious games and an Airbus-320 flight simulator. Third, we present a validation protocol that takes into account the levels of generalization of models based on the source and amount of test data.
Finally, the approaches for the assessment of cognitive states are applied to two use cases of high social impact: the assessment of mental workload for personalized support systems in the cockpit and the detection of epileptic seizures. The results obtained from the first use case show the feasibility of task transfer of models trained to detect workload in serious games to real flight scenarios. The results from the second use case show the generalization capability of our EEG channel fusion methods at k-fold cross-validation, patient-specific, and population levels. |
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Ph.D. thesis |
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IMPRIMA |
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Aura Hernandez;Debora Gil |
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Admin @ si @ Yau2023 |
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3962 |
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