2023 |
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Debora Gil, Guillermo Torres, & Carles Sanchez. (2023)." Transforming radiomic features into radiological words" In IEEE International Symposium on Biomedical Imaging.
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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023)." Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules" In 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery.
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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023)." Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project" . International Journal of Computer Assisted Radiology and Surgery, .
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Guillermo Torres, Debora Gil, Antonio Rosell, Sonia Baeza, & Carles Sanchez. (2023)." A radiomic biopsy for virtual histology of pulmonary nodules" In IEEE International Symposium on Biomedical Imaging.
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Guillermo Torres, Jan Rodríguez Dueñas, Sonia Baeza, Antoni Rosell, Carles Sanchez, & Debora Gil. (2023). "Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images " In 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023.
Abstract: 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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Juan Borrego-Carazo, Carles Sanchez, David Castells, Jordi Carrabina, & Debora Gil. (2023). "BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation " . Computer Methods and Programs in Biomedicine, 228, 107241.
Abstract: Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.
Keywords: Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation
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Pau Cano, Alvaro Caravaca, Debora Gil, & Eva Musulen. (2023). "Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images ".
Abstract: This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
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Pau Cano, Debora Gil, & Eva Musulen. (2023)." Towards automatic detection of helicobacter pylori in histological samples of gastric tissue" In IEEE International Symposium on Biomedical Imaging.
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Sonia Baeza, Debora Gil, Carles Sanchez, Guillermo Torres, Ignasi Garcia Olive, Ignasi Guasch, et al. (2023)." Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung" In SEPAR.
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Sonia Baeza, Debora Gil, Ignasi Garcia Olive, Maite Salcedo Pujantell, Jordi Deportos, Carles Sanchez, et al. (2023). "Correction: A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients " . European Journal of Nuclear Medicine and Molecular Imaging, 10(1), 13.
Abstract: 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.
Keywords: early diagnosis; Lung Cancer; nodule diagnosis; nodule diagnosis; Radiomics; Screening
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