An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semi-parametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach

A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images

A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach

Desarrollo de una herramienta para la evaluación de los examen tipo test y sus aplicaciones en la mejora de la calidad y en los criterios de evaluación de estos exámenes

Depto. de Radiología, Rehabilitación y FisioterapiaFac. de MedicinaFALSEsubmitte

A case study on sepsis using PubMed and Deep Learning for ontology learning

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora.Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits

Development and clinical evaluation of a simple optical method to detect and measure patient external motion

A simple and independent system to detect and measure the position of a number of points in space was devised and implemented. Its application aimed to detect patient motion during radiotherapy treatments, alert of out-of-tolerances motion, and record the trajectories for subsequent studies. The system obtains the 3D position of points in space, through its projections in 2D images recorded by two cameras. It tracks black dots on a white sticker placed on the surface of the moving object. The system was tested with linear displacements of a phantom, circular trajectories of a rotating disk, oscillations of an in-house phantom, and oscillations of a 4D phantom. It was also used to track 461 trajectories of points on the surface of patients during their radiotherapy treatments. Trajectories of several points were reproduced with accuracy better than 0.3 mm in the three spatial directions. The system was able to follow periodic motion with amplitudes lower than 0.5 mm, to follow trajectories of rotating points at speeds up to 11.5 cm/s, and to track accurately the motion of a respiratory phantom. The technique has been used to track the motion of patients during radiotherapy and to analyze that motion. The method is flexible. Its installation and calibration are simple and quick. It is easy to use and can be implemented at a very affordable price. Data collection does not involve any discomfort to the patient and does not delay the treatment, so the system can be used routinely in all treatments. It has an accuracy similar to that of other, more sophisticated, commercially available systems. It is suitable to implement a gating system or any other application requiring motion detection, such as 4D CT, MRI or PET

Human matrix metalloproteinases: An ubiquitarian class of enzymes involved in several pathological processes

Human matrix metalloproteinases (MMPs) belong to the M10 family of the MA clan of endopeptidases. They are ubiquitarian enzymes, structurally characterized by an active site where a Zn(2+) atom, coordinated by three histidines, plays the catalytic role, assisted by a glutamic acid as a general base. Various MMPs display different domain composition, which is very important for macromolecular substrates recognition. Substrate specificity is very different among MMPs, being often associated to their cellular compartmentalization and/or cellular type where they are expressed. An extensive review of the different MMPs structural and functional features is integrated with their pathological role in several types of diseases, spanning from cancer to cardiovascular diseases and to neurodegeneration. It emerges a very complex and crucial role played by these enzymes in many physiological and pathological processes