Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of...
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ISBN:
(纸本)9781509066391
Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations, when data are missing or inaccurate. This paper presents a general framework based on probabilistic graphical models to exploit social network structures for improving individual predictions of node attributes. Using this framework, we quantify the assortativity range leading to an accuracy gain in several situations. We finally show how specific characteristics of the network can improve performances further. For instance, the gender assortativity in real-world mobile phone data changes significantly according to some communication attributes. In this case, individual predictions with 75% accuracy are improved by up to 3%.
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse mul...
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segment...
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PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive w...
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PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo
BACKGROUND:Mental disorders are highly prevalent among students worldwide. This study aims to examine comorbidity and temporal associations between mental disorders among students.METHODS:The study included 72,288 stu...
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BACKGROUND:Mental disorders are highly prevalent among students worldwide. This study aims to examine comorbidity and temporal associations between mental disorders among students.
METHODS:The study included 72,288 students from 18 countries as part of the World Mental Health International College Student (WMH-ICS) Initiative, with cross-sectional data collected between 2017 and 2023. Screening for common DSM-5 disorders was conducted using validated screening measures. Latent variables were examined using exploratory principal axis factor analysis on a correlation matrix among the lifetime mental disorders. Based on age-of-onset information, multivariable poisson regression models were used to examine associations of prior disorders with the first onset of other disorders.
RESULTS:27.0 % of students screened positive for only one lifetime disorder, 17.1 % for two, 10.9 % for three, and 10.6 % for 4+ disorders. In the factor analysis, three latent variables were found, comprising: internalizing disorders (generalized anxiety disorder, major depressive episode, post-traumatic stress disorder, and panic disorder), substance use disorders (drug use disorder and alcohol use disorder), and externalizing disorders (attention deficit/hyperactivity disorder and mania/hypomania). Prior internalizing and externalizing disorders were associated with the subsequent first onset of all other disorders with risk ratios ranging from 1.5-7.5. Substance use disorders were less consistently associated with the subsequent first onset of other disorders, but alcohol use disorder was associated with the first onset of drug use disorder and vice versa.
CONCLUSIONS:Mental disorder comorbidity is common among students, and students with disorders across the internalizing and externalizing spectrum have an increased risk of future mental disorder comorbidities.
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combinin...
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Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it ...
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Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subjectspecific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.
We present the first Fermi-Large Area Telescope (LAT) solar flare catalog covering the 24th solar cycle. This catalog contains 45 Fermi-LAT solar flares (FLSFs) with emission in the γ-ray energy band (30 MeV - 10 GeV...
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