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Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
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Title |
Selection of Radiomics Features based on their Reproducibility |
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2019 |
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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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403-408 |
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Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. |
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Berlin; Alemanya; July 2019 |
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EMBC |
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IAM; 600.139; 600.145 |
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Admin @ si @ LTS2019 |
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3358 |
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Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
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Title |
Back to Front Architecture for Diagnosis as a Service |
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2018 |
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20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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343-346 |
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Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. |
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Timisoara; Rumania; September 2018 |
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SYNASC |
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IAM; 600.145 |
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Admin @ si @ SVA2018 |
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3360 |
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Author |
Debora Gil; Guillermo Torres |
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A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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2020 |
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34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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Virtual; June 2020 |
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CARS |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiT2020 |
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3472 |
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Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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2020 |
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Women in Geometry and Topology |
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Barcelona; September 2019 |
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IAM; DAG; 600.139; 600.145; 600.121 |
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Admin @ si @ GRP2020 |
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3473 |
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Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
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Title |
Localizing Pulmonary Lesions Using Fuzzy Deep Learning |
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2019 |
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21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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290-294 |
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The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. |
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Timisoara; Rumania; September 2019 |
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SYNASC |
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IAM; 600.145; 600.140; 601.337; 601.323 |
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Admin @ si @ RSG2019 |
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3531 |
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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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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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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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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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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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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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Admin @ si @ CGM2023 |
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3953 |
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