756 research outputs found
A fuzzy-based system reveals Alzheimer’s Disease onset in subjects with Mild Cognitive Impairment
No full textComplex Network Modelling of Origin–Destination Commuting Flows for the COVID-19 Epidemic Spread Analysis in Italian Lombardy Region
No full textCurrently the whole world is affected by the COVID-19 disease. Italy was the first country to be seriously affected in Europe, where the first COVID-19 outbreak was localized in the Lombardy region. The further spreading of the cases led to the lockdown of the most affected regions in northern Italy and then the entire country. In this work we investigated an epidemic spread scenario in the Lombardy region by using the origin–destination matrix with information about the commuting flows among 1450 urban areas within the region. We performed a large-scale simulation-based modeling of the epidemic spread over the networks related to three main motivations, i.e., work, study and occasional transfers to quantify the potential contribution of each category of travellers to the spread of the epidemic process. Our findings outline that the three networks are characterised by different weight dynamic growth rates and that the network “work” has a critical role in the diffusion phenomenon showing the greatest contribution to the epidemic spread
Tackling the small data problem in medical image classification with artificial intelligence: a systematic review
No full textBackground: Though medical imaging has seen a growing interest in AI research, training models
require a large amount of data. In this domain, there are limited sets of data available as collecting
new data is either not feasible or requires burdensome resources. Researchers are facing with the
problem of small datasets and have to apply tricks to fight overfitting.
Methods: 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31
July 2022 and articles were assessed by two independent reviewers. We followed the PRISMA
guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review.
Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of
a multivariable prediction model for individual prognosis or diagnosis).
Results: To solve the small data issue transfer learning technique, basic data augmentation and GAN
were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a
binary classification given the data scarcity and the difficulty of the tasks. Concerning
generalizability, only 4 studies explicitly stated an external validation of the developed model was
carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of
studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD
items).
Conclusion: The goal of this review is to provide a comprehensive survey of recent advancements in
dealing with small medical images samples size. Transparency and improve quality in publications as
well as follow existing reporting standards are also supported
Sabina Murray, 30th Annual ODU Literary Festival
No full textSabina Murray is the award-winning author of the novels Slow Burn, and A Carnivore’s Inquiry, and the story collection The Caprices. A former Michener Fellow at the University of Texas and Bunting Fellow at the Radcliffe Institute of Harvard University, she received the PEN/Faulkner Award for Fiction in 2003. Murray’s stories have appeared in Ploughshares, Ontario Review, the New England Review, and other literary journals. Currently, she teaches in the MFA program at the University of Massachusetts, Amherst
Explainable Deep Learning for Personalized Age Prediction with Brain Morphology
No full textPredicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. (c) Copyright (c) 2021 Lombardi, Diacono, Amoroso, Monaco, Tavares, Bellotti and Tangaro
Sabina baccifera, Sabina sterile, Miride
No full text1-2. Nome scientifico: Juniperus sabina L.
(Cupressaceae)
Nome attuale: Sabina, Ginepro sabino
3. Nome scientifico: Myrrhis odorata (L.) Scop.
(Apiaceae, Umbelliferae)
Nome attuale: Mirrid
Editorial: Explainable Artificial Intelligence (XAI) in Systems Neuroscience
No full textIn the last 10 years, we have experienced exceptional growth in the development of machine-learning-based (ML) algorithms for the analysis of different medical conditions and for developing clinical decision support systems. In particular, the availability of large datasets and the increasing complexity of both hardware and software systems have enabled the emergence of the new multidisciplinary field of computational neuroscience (Teeters et al., 2008). Sophisticated machine learning algorithms can be trained using brain imaging data to classify neurodegenerative disorders, detect neuropsichiatric conditions (Davatzikos, 2019), and perform accurate brain age prediction for the identification of novel functional and structural biomarkers for different diseases (Cole and Franke, 2017). In this Research Topic, we collected several original research works where different XAI techniques were embedded in both ML and DL algorithms for the extraction of reliable biomarkers from neuroimaging datasets for several predictive tasks
I remember Farm Center at Seabrook
No full textIn this "I remember" memoir, Sabina Slavic Woodward recalls how Charles Seabrook sent a representative to refugee camps in West Germany to recruit workers. Sabina's family signed up, and spent 10 days at sea. Many of the refugees were malnourished. During the first year at Seabrook, Sabina's family lived in Farm Center, which was a cramped living area that lacked privacy. Once her parents were able to gain more hours to earn more money, her family was able to move into a more suitable house. Tragically, in 1955, her older brother, Franz, drowned in a nearby lake. The Seabrook Educational and Cultural Center has been soliciting current and past residents of Seabrook Farms for an "I remember" project. Residents are asked to create narratives regarding their experiences at Seabrook Farms. These memories help preserve the history and multi-cultural heritage of Seabrook Farms
Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019
No full textMorphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results
Communicability disruption in Alzheimer's disease connectivity networks
No full textIn real-world networks, information from source to destination does not only flow along the shortest path connecting them, but can flow along any alternative route. Communicability is a network metric that accounts for this issue and, especially in diffusion-like processes, provides a reliable measure of the ease of communication between node pairs. Accordingly, communicability appears to be promising for highlighting the disruption of connectivity among brain regions, caused by the white matter degeneration due to Alzheimer's disease (AD). Such a degeneration can be captured by digital imaging techniques, in particular diffusion tensor imaging (DTI), which allow to build the brain connectivity network through tractography algorithms and studying its complexity through graph theory. In this study, a cohort of 122 DTI scans, composed by 52 healthy control (HC) subjects, 40 AD patients and 30 mild cognitive impairment (MCI) converter subjects, from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, has been employed to study the suitability of communicability to serve as discriminant factor for AD. We developed a two-fold investigation. On one hand, a statistical analysis has been carried out to ascertain the information content provided by communicability to detect the brain regions mostly affected by the disease: node pairs with statistical significant different communicability have been found, corresponding
- …
