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Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
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Title |
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
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Journal Article |
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2022 |
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Journal of Thoracic Oncology |
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JTO |
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17 |
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9 |
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S182 |
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Admin @ si @ RBG2022b |
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3834 |
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Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
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Title |
Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. |
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Journal Article |
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2022 |
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European Respiratory Journal |
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ERJ |
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60 |
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66 |
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Admin @ si @ RBG2022c |
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3835 |
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Author |
Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil |
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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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Year |
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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Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
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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 |
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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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Author |
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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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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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 |
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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 |
Debora Gil; Aura Hernandez-Sabate; Antoni Carol; Oriol Rodriguez; Petia Radeva |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
A Deterministic-Statistic Adventitia Detection in IVUS Images |
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Conference Article |
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2005 |
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ESC Congress |
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Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation |
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Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles. |
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Stockholm; Sweden; September 2005 |
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IAM;MILAB |
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IAM @ iam @ RMF2005a |
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1523 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging |
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Conference Article |
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Year |
2005 |
Publication |
Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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67-74 |
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Keywords |
classification; vessel border modelling; IVUS |
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Abstract |
IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. |
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IOS Press |
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Amsterdam, The Netherlands |
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IAM;MILAB |
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IAM @ iam @ HGR2005c |
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1549 |
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