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61دورية أكاديمية
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62دورية أكاديمية
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63دورية أكاديمية
المؤلفون: Bartko, H., Perrin, G., Brandner, W., Straubmeier, C., Richichi, A., Gillessen, S., Paumard, T., Hippler, S., Eckart, A., Schöller, M., Eisenhauer, F., Haubois, X., Lenzen, R., Rabien, S., Clénet, Y., Ramos, J.R., Thiel, M., Berger, J.P., Baumeister, H., Kellner, S., Cassaing, F., Böhm, A., Hofmann, R., Gendron, E., Klein, R., Dodds-Eden, K., Houairi, K., Hormuth, F., Gräter, A., Kervella, P., Naranjo, V., Genzel, R., Fédou, P., Henning, T., Hamaus, N., Jocou, L., Neumann, U., Haug, M., Lacour, S., Laun, W., Kolmeder, J., Malbet, F., Rohloff, R.-R., Pfuhl, O., Perraut, K., Ziegleder, J., Rouan, D., Rousset, G., Amorim, A., Lima, J.
المصدر: In New Astronomy Reviews 2009 53(11):301-306
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64
المؤلفون: Bernabeu-Sanz A, Morales S, Naranjo V, Sempere A
المصدر: DIAGNOSTICS
r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante
instnameمصطلحات موضوعية: diffusion tensor imaging, thalami atrophy, relapsing-remitting multiple sclerosis, white matter damage, cognitive impairment
الوصف: Cognitive impairment (CI) is frequently present in multiple sclerosis patients. Despite ongoing research, the neurological substrates have not been fully elucidated. In this study we investigated the contribution of gray and white matter in the CI observed in mildly disabled relapsing-remitting multiple sclerosis (RRMS) patients. For that purpose, 30 patients with RRMS (median EDSS = 2), and 30 age- and sex-matched healthy controls were studied. CI was assessed using the symbol digit modalities test (SDMT) and the memory alteration test. Brain magnetic resonance imaging, diffusion tensor imaging (DTI), voxel-based morphometry (VBM), brain segmentation, thalamic vertex analysis, and connectivity-based thalamic parcellation analyses were performed. RRMS patients scored significantly lower in both cognitive tests. In the patient group, significant atrophy in the thalami was observed. Multiple regression analyses revealed associations between SDMT scores and GM volume in both hemispheres in the temporal, parietal, frontal, and occipital lobes. The DTI results pointed to white matter damage in all thalamocortical connections, the corpus callosum, and several fasciculi. Multiple regression and correlation analyses suggested that in RRMS patients with mild disease, thalamic atrophy and thalamocortical connection damage may lead to slower cognitive processing. Furthermore, white matter damage at specific fasciculi may be related to episodic memory impairment.
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المؤلفون: del Amor R, Launet L, Colomer A, Moscardo A, Mosquera-Zamudio A, Monteagudo C, Naranjo V
المصدر: ARTIFICIAL INTELLIGENCE IN MEDICINE
r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
instnameمصطلحات موضوعية: Multiple instance learning, Attention convolutional neural network, Histopathological whole-slide images, Inductive transfer learning, Spitzoid lesions
الوصف: Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a highly time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no system allows both the selection of the tumor region and the prediction of the benign or malignant form in the diagnosis. Motivated by this, we propose a novel end-to-end weakly supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we performed extensive experiments on a private skin database with spitzoid lesions. Test results achieved an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. In addition, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist.
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66Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture
المؤلفون: Noorda R, Nevárez A, Colomer A, Pons Beltrán V, Naranjo V
المصدر: SCIENTIFIC REPORTS
r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe
instnameالوصف: Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effective preparation method are conflicting due to the absence of objective, automatic cleanliness evaluation methods. In this work, we aim to provide such a method capable of presenting results on an intuitive scale, with a relatively light-weight novel convolutional neural network architecture at its core. We trained our model using 5-fold cross-validation on an extensive data set of over 50,000 image patches, collected from 35 different CE procedures, and compared it with state-of-the-art classification methods. From the patch classification results, we developed a method to automatically estimate pixel-level probabilities and deduce cleanliness evaluation scores through automatically learnt thresholds. We then validated our method in a clinical setting on 30 newly collected CE videos, comparing the resulting scores to those independently assigned by human specialists. We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. In the validation of our method, we found acceptable agreement with two human specialists compared to interhuman agreement, showing its validity as an objective evaluation method.
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المؤلفون: Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, Meseguer M
المصدر: FERTILITY AND STERILITY
r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe
instnameمصطلحات موضوعية: body regions, Embryo parameters, artificial intelligence, artificial neural network, implantation, time-lapse, ComputingMethodologies_PATTERNRECOGNITION, animal structures, Quantitative Biology::Tissues and Organs, embryonic structures, Computer Science::Neural and Evolutionary Computation, equipment and supplies
الوصف: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.
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68دورية أكاديمية
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69دورية أكاديمية
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70دورية أكاديمية
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