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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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ISBI |
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Admin @ si @ CGM2023 |
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3953 |
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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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Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
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2023 |
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Granada; Spain; June 2023 |
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Admin @ si @ BGS2023 |
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3951 |
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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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EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
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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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Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. |
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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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Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
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Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
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Journal Article |
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2022 |
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Applied Sciences |
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APPLSCI |
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12 |
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5 |
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2298 |
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Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
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The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
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February 2022 |
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IAM; ADAS; 600.139; 600.145; 600.118 |
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Admin @ si @ HYF2022 |
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3720 |
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David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina |
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A Survey of FPGA-Based Vision Systems for Autonomous Cars |
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Journal Article |
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2022 |
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IEEE Access |
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ACESS |
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10 |
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132525-132563 |
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Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures |
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On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. |
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16 December 2022 |
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IEEE |
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IAM; 600.166 |
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Admin @ si @ CNB2022 |
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3760 |
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Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera |
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Title |
E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights |
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2022 |
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IEEE Access |
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ACCESS |
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10 |
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7489-7503 |
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More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches. |
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IAM; 600.139; 600.118; 600.145 |
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Admin @ si @ GHE2022 |
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3721 |
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Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil |
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An Intelligent Radiomic Approach for Lung Cancer Screening |
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Journal Article |
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2022 |
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Applied Sciences |
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APPLSCI |
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12 |
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3 |
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1568 |
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Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization |
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The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection. |
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Jan 2022 |
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IAM; 600.139; 600.145 |
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Admin @ si @ TBS2022 |
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3699 |
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Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
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A benchmark for the evaluation of computational methods for bronchoscopic navigation |
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2022 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCARS |
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17 |
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1 |
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Admin @ si @ BSC2022 |
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3832 |
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Miquel Angel Piera; Jose Luis Muñoz; Debora Gil; Gonzalo Martin; Jordi Manzano |
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A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance |
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2022 |
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IEEE Access |
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ACCESS |
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10 |
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22330-22343 |
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Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance |
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The future deployment of single pilot operations must be supported by new cockpit computer services. Such services require an adaptive context-aware integration of technical functionalities with the concurrent tasks that a pilot must deal with. Advanced artificial intelligence supporting services and improved communication capabilities are the key enabling technologies that will render future cockpits more integrated with the present digitalized air traffic management system. However, an issue in the integration of such technologies is the lack of socio-technical analysis in the design of these teaming mechanisms. A key factor in determining how and when a service support should be provided is the dynamic evolution of pilot workload. This paper investigates how the socio-technical model-based systems engineering approach paves the way for the design of a digital assistant framework by formalizing this workload. The model was validated in an Airbus A-320 cockpit simulator, and the results confirmed the degraded pilot behavioral model and the performance impact according to different contextual flight deck information. This study contributes to practical knowledge for designing human-machine task-sharing systems. |
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Feb 2022 |
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Admin @ si @ PMG2022 |
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3697 |
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