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	Author	Miquel Angel Piera; Jose Luis Muñoz; Debora Gil; Gonzalo Martin; Jordi Manzano
	Title	A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance			Type	Journal Article
	Year	2022	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	10	Issue		Pages	22330-22343
	Keywords	Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance
	Abstract	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.
	Address	Feb 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM;			Approved	no
	Call Number	Admin @ si @ PMG2022			Serial	3697
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	Author	Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil
	Title	An Intelligent Radiomic Approach for Lung Cancer Screening			Type	Journal Article
	Year	2022	Publication	Applied Sciences	Abbreviated Journal	APPLSCI
	Volume	12	Issue	3	Pages	1568
	Keywords	Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization
	Abstract	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.
	Address	Jan 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; 600.139; 600.145			Approved	no
	Call Number	Admin @ si @ TBS2022			Serial	3699
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	Author	Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach
	Title	Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge			Type	Journal Article
	Year	2022	Publication	Gastrointestinal Endoscopy	Abbreviated Journal
	Volume	96	Issue	2	Pages	370-372
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ MMB2022			Serial	3701
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	Author	Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera
	Title	First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis			Type	Journal Article
	Year	2022	Publication	IEEE Transactions on Affective Computing	Abbreviated Journal	TAC
	Volume	13	Issue	1	Pages	75-95
	Keywords	Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition
	Abstract	Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.
	Address	1 Jan.-March 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Admin @ si @ JGP2022			Serial	3724
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	Author	Oriol Ramos Terrades; Albert Berenguel; Debora Gil
	Title	A Flexible Outlier Detector Based on a Topology Given by Graph Communities			Type	Journal Article
	Year	2022	Publication	Big Data Research	Abbreviated Journal	BDR
	Volume	29	Issue		Pages	100332
	Keywords	Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors
	Abstract	Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. 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, like the number of neighbors. 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 and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.
	Address	August 28, 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159			Approved	no
	Call Number	Admin @ si @ RBG2022a			Serial	3718
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	Author	Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; B. Cardenas; G. Fonseka; E. Anton; Alvaro Pascual; Richard Frodsham; Zaida Sarrate
	Title	Time to match; when do homologous chromosomes become closer?			Type	Journal Article
	Year	2022	Publication	Chromosoma	Abbreviated Journal	CHRO
	Volume		Issue		Pages
	Keywords
	Abstract	In most eukaryotes, pairing of homologous chromosomes is an essential feature of meiosis that ensures homologous recombination and segregation. However, when the pairing process begins, it is still under investigation. Contrasting data exists in Mus musculus, since both leptotene DSB-dependent and preleptotene DSB-independent mechanisms have been described. To unravel this contention, we examined homologous pairing in pre-meiotic and meiotic Mus musculus cells using a threedimensional fuorescence in situ hybridization-based protocol, which enables the analysis of the entire karyotype using DNA painting probes. Our data establishes in an unambiguously manner that 73.83% of homologous chromosomes are already paired at premeiotic stages (spermatogonia-early preleptotene spermatocytes). The percentage of paired homologous chromosomes increases to 84.60% at mid-preleptotene-zygotene stage, reaching 100% at pachytene stage. Importantly, our results demonstrate a high percentage of homologous pairing observed before the onset of meiosis; this pairing does not occur randomly, as the percentage was higher than that observed in somatic cells (19.47%) and between nonhomologous chromosomes (41.1%). Finally, we have also observed that premeiotic homologous pairing is asynchronous and independent of the chromosome size, GC content, or presence of NOR regions.
	Address	August, 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; 601.139; 600.145; 600.096			Approved	no
	Call Number	Admin @ si @ SBG2022			Serial	3719
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	Author	Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil
	Title	Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals			Type	Journal Article
	Year	2022	Publication	Applied Sciences	Abbreviated Journal	APPLSCI
	Volume	12	Issue	5	Pages	2298
	Keywords	Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion
	Abstract	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.
	Address	February 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; ADAS; 600.139; 600.145; 600.118			Approved	no
	Call Number	Admin @ si @ HYF2022			Serial	3720
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	Author	Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera
	Title	E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights			Type	Journal Article
	Year	2022	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	10	Issue		Pages	7489-7503
	Keywords
	Abstract	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.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; 600.139; 600.118; 600.145			Approved	no
	Call Number	Admin @ si @ GHE2022			Serial	3721
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	Author	Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi
	Title	Few shots are all you need: A progressive learning approach for low resource handwritten text recognition			Type	Journal Article
	Year	2022	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	160	Issue		Pages	43-49
	Keywords
	Abstract	Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121; 600.162; 602.230			Approved	no
	Call Number	Admin @ si @ SFK2022			Serial	3736
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	Author	Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera
	Title	Deep learning with self-supervision and uncertainty regularization to count fish in underwater images			Type	Journal Article
	Year	2022	Publication	PloS One	Abbreviated Journal	Plos
	Volume	17	Issue	5	Pages	e0267759
	Keywords
	Abstract	Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data.
	Address
	Corporate Author				Thesis
	Publisher	Public Library of Science	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Admin @ si @ TCC2022			Serial	3743
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	Author	Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez
	Title	Image rain removal and illumination enhancement done in one go			Type	Journal Article
	Year	2022	Publication	Knowledge-Based Systems	Abbreviated Journal	KBS
	Volume	252	Issue		Pages	109244
	Keywords
	Abstract	Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement.
	Address	Sept 2022
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; 600.157; 600.168			Approved	no
	Call Number	Admin @ si @ WCS2022			Serial	3744
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	Author	Lu Yu; Xialei Liu; Joost Van de Weijer
	Title	Self-Training for Class-Incremental Semantic Segmentation			Type	Journal Article
	Year	2022	Publication	IEEE Transactions on Neural Networks and Learning Systems	Abbreviated Journal	TNNLS
	Volume		Issue		Pages
	Keywords	Class-incremental learning; Self-training; Semantic segmentation.
	Abstract	In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.147; 611.008;			Approved	no
	Call Number	Admin @ si @ YLW2022			Serial	3745
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	Author	Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir
	Title	Minimising multi-centre radiomics variability through image normalisation: a pilot study			Type	Journal Article
	Year	2022	Publication	Scientific Reports	Abbreviated Journal	ScR
	Volume	12	Issue	1	Pages	12532
	Keywords
	Abstract	Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
	Address	2022/07/22
	Corporate Author				Thesis
	Publisher	Springer Nature	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Admin @ si @ CMI2022			Serial	3749
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	Author	Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach
	Title	In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy			Type	Journal Article
	Year	2022	Publication	Endoscopy International Open	Abbreviated Journal	ENDIO
	Volume	10	Issue	9	Pages	E1201-E1207
	Keywords
	Abstract	Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions.
	Address	2022 Sep 14
	Corporate Author				Thesis
	Publisher	PMID	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; 600.157			Approved	no
	Call Number	Admin @ si @ GTC2022b			Serial	3752
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	Author	Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey
	Title	Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms			Type	Journal Article
	Year	2022	Publication	Journal of Archaeological Science	Abbreviated Journal	JArchSci
	Volume	148	Issue		Pages	105654
	Keywords
	Abstract	This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability.
	Address	December 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU; MACO; 600.167			Approved	no
	Call Number	Admin @ si @ BOL2022			Serial	3753
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