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F. Javier Sanchez; Jordi Vitria; Enric Marti |
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Transformaciones Morfológicas de Polígonos Isotéticos |
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1991 |
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Primer Congreso Español de Informática Gráfica. |
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OR;IAM;MV |
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IAM @ iam @ SVM1991 |
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1648 |
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Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca |
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Title |
Transformer based Image Dehazing |
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2022 |
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16th IEEE International Conference on Signal Image Technology & Internet Based System |
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atmospheric light; brightness component; computational cost; dehazing quality; haze-free image |
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This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models. |
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Dijon; France; October 2022 |
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SITIS |
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MSIAU; no proj |
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Admin @ si @ SCS2022 |
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3803 |
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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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B. Gotschy; Matthias S. Keil; H. Klos; I. Rystau |
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Transition from static to dynamic Jahn-Teller distortion in (P(C6 H5)4)2 C60| |
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1994 |
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Solid State Communications, 92(12): 935–938 |
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Admin @ si @ GKK1994 |
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631 |
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Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera |
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Title |
Tri-modal Person Re-identification with RGB, Depth and Thermal Features |
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2013 |
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9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition |
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301-307 |
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Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios. |
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Portland; oregon; June 2013 |
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978-0-7695-4990-3 |
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CVPRW |
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HUPBA;MILAB |
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no |
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Admin @ si @ MBM2013 |
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2253 |
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Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui; Jian Yang |
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Title |
Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering |
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Journal Article |
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2023 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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45 |
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12 |
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15883-15895 |
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Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g., due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. |
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LAMP; MACO |
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no |
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Admin @ si @ YWW2023 |
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3889 |
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Author |
A. Auge; Javier Varona; Juan J. Villanueva |
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Title |
Tumour Segmentation in Mammographies with Neural Networks. Application to Tumoural Volume Approximation. |
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Journal Article |
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1997 |
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Proceedings of the VII NSPRIA, Vol. II, CVC–UAB |
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ISE @ ise @ AVV1997 |
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208 |
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Author |
Judit Martinez; Eva Costa; P. Herreros; Antonio Lopez; Juan J. Villanueva |
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Title |
TV-Screen Quality Inspection by Artificial Vision |
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Conference Article |
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2003 |
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Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003) |
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A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions. |
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Gatlinburg, (EEUU) |
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ADAS |
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ADAS @ adas @ MCH2003a |
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393 |
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Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
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TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
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2022 |
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26th International Conference on Pattern Recognition |
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Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
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Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
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Montreal; Quebec; Canada; August 2022 |
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ICPR |
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DAG; |
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no |
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Admin @ si @ BSA2022 |
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3788 |
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Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari |
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Title |
Two-stage Recognition and Beyond for Compound Facial Emotion Recognition |
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2021 |
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Electronics |
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ELEC |
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10 |
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22 |
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2847 |
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compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning |
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Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels. |
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HUPBA; no proj |
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Admin @ si @ KAR2021 |
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3642 |
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Author |
Jose Seabra; F. Javier Sanchez; Francesco Ciompi; Petia Radeva |
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Title |
Ultrasonographic Plaque Characterization using a Rayleigh Mixture Model |
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2010 |
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7th IEEE International Symposium on Biomedical Imaging |
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1–4 |
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From Nano to Macro
A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%, 94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution. |
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Rotterdam (Netherlands) |
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1945-7928 |
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978-1-4244-4125-9 |
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ISBI |
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MILAB |
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BCNPCL @ bcnpcl @ SSC2010 |
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1366 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Ultrasound to magnetic resonance volume registration for brain sinking measurement |
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Miscellaneous |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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Palma de Mallorca |
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ADAS @ adas @ LSL2003b |
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391 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Ultrasound to MR Volume Registration for Brain Sinking Measurement |
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2003 |
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In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, 2652:420–427 |
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Springer-Verlag |
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ADAS @ adas @ LSL2003a |
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384 |
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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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Ultrathin Bronchoscopy with and without Virtual Bronchoscopic Navigation: Influence of Segmentation on Diagnostic Yield |
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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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Joan Oliver; Ricardo Toledo; J. Pujol; J. Sorribes; E. Valderrama |
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Un ABP basado en la robotica para las ingenierias informaticas |
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2009 |
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15th Jornadas de Enseñanza Universitaria de la Informatica |
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331–338 |
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Barcelona, Spain |
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ISBN:978–84–692–2758–9 |
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