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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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A flexible outlier detector based on a topology given by graph communities |
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Miscellaneous |
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2020 |
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Arxiv |
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Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. |
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IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
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Admin @ si @ RBG2020 |
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3475 |
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Author |
Guillermo Torres; Debora Gil |
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A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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Journal Article |
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2020 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCAR |
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15 |
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1 |
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S154-55 |
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IAM |
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Admin @ si @ ToG2020 |
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3590 |
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Author |
Debora Gil; Ruth Aris; Agnes Borras; Esmitt Ramirez; Rafael Sebastian; Mariano Vazquez |
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Title |
Influence of fiber connectivity in simulations of cardiac biomechanics |
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Journal Article |
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2019 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCAR |
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14 |
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1 |
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63–72 |
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Cardiac electromechanical simulations; Diffusion tensor imaging; Fiber connectivity |
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PURPOSE:
Personalized computational simulations of the heart could open up new improved approaches to diagnosis and surgery assistance systems. While it is fully recognized that myocardial fiber orientation is central for the construction of realistic computational models of cardiac electromechanics, the role of its overall architecture and connectivity remains unclear. Morphological studies show that the distribution of cardiac muscular fibers at the basal ring connects epicardium and endocardium. However, computational models simplify their distribution and disregard the basal loop. This work explores the influence in computational simulations of fiber distribution at different short-axis cuts.
METHODS:
We have used a highly parallelized computational solver to test different fiber models of ventricular muscular connectivity. We have considered two rule-based mathematical models and an own-designed method preserving basal connectivity as observed in experimental data. Simulated cardiac functional scores (rotation, torsion and longitudinal shortening) were compared to experimental healthy ranges using generalized models (rotation) and Mahalanobis distances (shortening, torsion).
RESULTS:
The probability of rotation was significantly lower for ruled-based models [95% CI (0.13, 0.20)] in comparison with experimental data [95% CI (0.23, 0.31)]. The Mahalanobis distance for experimental data was in the edge of the region enclosing 99% of the healthy population.
CONCLUSIONS:
Cardiac electromechanical simulations of the heart with fibers extracted from experimental data produce functional scores closer to healthy ranges than rule-based models disregarding architecture connectivity. |
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IAM; 600.096; 601.323; 600.139; 600.145 |
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Admin @ si @ GAB2019a |
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3133 |
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Marta Diez-Ferrer; Arturo Morales; Rosa Lopez Lisbona; Noelia Cubero; Cristian Tebe; Susana Padrones; Samantha Aso; Jordi Dorca; Debora Gil; Antoni Rosell |
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Title |
Ultrathin Bronchoscopy with and without Virtual Bronchoscopic Navigation: Influence of Segmentation on Diagnostic Yield |
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Journal Article |
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2019 |
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Respiration |
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RES |
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97 |
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3 |
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252-258 |
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Lung cancer; Peripheral lung lesion; Diagnosis; Bronchoscopy; Ultrathin bronchoscopy; Virtual bronchoscopic navigation |
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Background: Bronchoscopy is a safe technique for diagnosing peripheral pulmonary lesions (PPLs), and virtual bronchoscopic navigation (VBN) helps guide the bronchoscope to PPLs. Objectives: We aimed to compare the diagnostic yield of VBN-guided and unguided ultrathin bronchoscopy (UTB) and explore clinical and technical factors associated with better results. We developed a diagnostic algorithm for deciding whether to use VBN to reach PPLs or choose an alternative diagnostic approach. Methods: We compared diagnostic yield between VBN-UTB (prospective cases) and unguided UTB (historical controls) and analyzed the VBN-UTB subgroup to identify clinical and technical variables that could predict the success of VBN-UTB. Results: Fifty-five cases and 110 controls were included. The overall diagnostic yield did not differ between the VBN-guided and unguided arms (47 and 40%, respectively; p = 0.354). Although the yield was slightly higher for PPLs ≤20 mm in the VBN-UTB arm, the difference was not significant (p = 0.069). No other clinical characteristics were associated with a higher yield in a subgroup analysis, but an 85% diagnostic yield was observed when segmentation was optimal and the PPL was endobronchial (vs. 30% when segmentation was suboptimal and 20% when segmentation was optimal but the PPL was extrabronchial). Conclusions: VBN-guided UTB is not superior to unguided UTB. A greater impact of VBN-guided over unguided UTB is highly dependent on both segmentation quality and an endobronchial location of the PPL. Segmentation quality should be considered before starting a procedure, when an alternative technique that may improve yield can be chosen, saving time and resources. |
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IAM; 600.145; 600.139 |
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Admin @ si @ DML2019 |
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3134 |
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Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Carles Sanchez |
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Title |
Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN |
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Journal Article |
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2019 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCAR |
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7 |
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1 |
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This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project. |
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IAM; 600.139; 600.145 |
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Admin @ si @ GEB2019 |
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3307 |
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Author |
Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell |
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Title |
Segmentation of Distal Airways using Structural Analysis |
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Journal Article |
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2019 |
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PloS one |
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Plos |
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14 |
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12 |
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Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. |
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IAM; 600.139; 600.145 |
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Admin @ si @ GSB2019 |
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3357 |
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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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Conference Article |
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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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Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez |
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Data Augmentation from Sketch |
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Conference Article |
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2019 |
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International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging |
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11840 |
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155-162 |
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Data augmentation; cycleGANs; Multi-objective optimization |
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State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
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Shenzhen; China; October 2019 |
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CLIP |
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IAM; 600.145; 601.337; 600.139; 600.145 |
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Admin @ si @ GES2019 |
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3359 |
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Debora Gil; Antoni Rosell |
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Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? |
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2019 |
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World Lung Cancer Conference |
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Barcelona; September 2019 |
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IASLC WCLC |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiR2019 |
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3361 |
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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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