Search

Your search keyword '"Liao, Wei"' showing total 11,418 results
Start Over Author "Liao, Wei"
11,418 results on '"Liao, Wei"'

158. Ein routine-integrierbares Planungswerkzeug zur operativen Rekonstruktion der Orbita

Author

Schulze, Dirk, Kleiner, Melanie E. H., Metzger, Marc C., Deserno, Thomas M., and Liao, Wei

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Infectious and parasitic diseases, RC109-216
Abstract

Bei der operativen Rekonstruktion des Orbitabodens ist unter anderem die Gleichheit von linkem und rechten Orbitavolumen für das Sehvermögen entscheidend. In dieser Arbeit wird ein Planungswerkzeug entwickelt, welches mit Hilfe eines aktiven Konturmodells den Orbitainhalt in CT-Datensätzen segmentiert, ihr Volumen berechnet und dieses visualisiert, um dem operierenden Arzt die Therapieplanung und -kontrolle zu erleichtern. Neben den technischen Integrationsstufen der Funktions- und Präsentationsintegration, die durch den Einsatz des Medical Imaging Interaction Toolkit (MITK) direkt erreicht werden, sowie der Daten-, und Kontextintegration, die durch das DICOM Protokoll unterstützt werden, ist vor allem die Stabilität, Robustheit und Korrektheit der eingesetzten Algorithmen für die Routine-Integrierbarkeit wichtig. Stabilitätsuntersuchungen bei 20 Patienten und jeweils 50 zufällig gewählten Startpunkten zeigen einen mittleren Variationskoeffizienten von 0,72%, d.h. deutlich unterhalb der kritischen 5% Schwelle. Die Robustheitsuntersuchung auf 200 Orbitae ergab eine Fehlerrate von 8%. Vergleiche der Volumina der rechten und linken Seite bei 74 klinisch unauffälligen Individuen resultiert in einem mittleren Variationskoeffizienten von 0,85%, so dass das Verfahren auch als korrekt angesehen werden kann. Exemplarisch wird die Anwendbarkeit mit einer klinischen Fallstudie belegt. Aufgrund der nachgewiesenen Stabilität und Korrektheit des Verfahrens sowie der umgesetzten Daten-, Funktions-, Kontext- und Präsentationsintegration kann das Tool in den klinischen Prozess eingebunden werden, und soll nun in der praktischen Anwendung umfassend evaluiert werden.

Published
2009

179. Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity

Author

Liao, Wei, Wei, Xiaohui, and Lai, Jizhou

Subjects
Computer Science - Artificial Intelligence
Abstract

An improvement of Q-learning is proposed in this paper. It is different from classic Q-learning in that the similarity between different states and actions is considered in the proposed method. During the training, a new updating mechanism is used, in which the Q value of the similar state-action pairs are updated synchronously. The proposed method can be used in combination with both tabular Q-learning function and deep Q-learning. And the results of numerical examples illustrate that compared to the classic Q-learning, the proposed method has a significantly better performance.

Published
2021

180. Very Large Array imaging rules out precessing radio jets in three DES$-$SDSS-selected candidate periodic quasars

Author

Chen, Yu-Ching, Liu, Xin, Liao, Wei-Ting, and Guo, Hengxiao

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract

Periodic quasars have been suggested as candidates for hosting binary supermassive black holes (SMBHs), although alternative scenarios remain possible to explain the optical light curve periodicity. To test the alternative hypothesis of precessing radio jet, we present deep 6 GHz radio imaging conducted with NSF's Karl G. Jansky Very Large Array (VLA) in its C configuration for the three candidate periodic quasars, DES J024703.24$-$010032.0, DES J024944.66$-$000036.8, and DES J025214.67$-$002813.7. Our targets were selected based on their optical variability using 20-yr long multi-color light curves from the Dark Energy Survey (DES) and the Sloan Digital Sky Survey (SDSS). The new VLA observations show that all three periodic quasars are radio-quiet with the radio loudness parameters measured to be $R\equiv f_{6\,{\rm cm}}/f_{{\rm 2500}}$ of $\lesssim$1.0$-$1.5 and the $k$-corrected luminosities $\nu L_\nu$[6 GHz] of $\lesssim$5$-$21 $\times$ 10$^{39}$ erg s$^{-1}$. They are in stark contrast to previously known periodic quasars proposed as binary SMBH candidates such as the blazar OJ 287 and PG1302$-$102. Our results rule out optical emission contributed from precessing radio jets as the origin of the optical periodicity in the three DES$-$SDSS-selected candidate periodic quasars. Future continued optical monitoring and complementary multi-wavelength observations are still needed to further test the binary SMBH hypothesis as well as other competing scenarios to explain the optical periodicity., Comment: 8 pages, 5 figures, 2 tables, accepted for publication in MNRAS

Published
2021
Full Text
View/download PDF

181. Spectral Machine Learning for Pancreatic Mass Imaging Classification

Author

Liu, Yiming, Chen, Ying, Pan, Guangming, Wang, Weichung, Liao, Wei-Chih, Thian, Yee Liang, Chee, Cheng E., and Anastassiades, Constantinos P.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, 62P10
Abstract

We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging. Our algorithm is trained with approximately 30,000 images from 250 patients (50 patients with normal pancreas and 200 patients with abnormal pancreas findings) based on public data sources. A test accuracy of 94.6 percents was achieved in the out-of-sample diagnosis classification based on a total of approximately 15,000 images from 113 patients, whereby 26 out of 32 patients with normal pancreas and all 81 patients with abnormal pancreas findings were correctly diagnosed. SML is able to automatically choose fundamental images (on average 5 or 9 images for each patient) in the diagnosis classification and achieve the above mentioned accuracy. The computational time is 75 seconds for diagnosing 113 patients in a laptop with standard CPU running environment. Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest. Our methodology showcases practical utility and improved accuracy of image diagnosis in pancreatic mass screening in the era of AI., Comment: 17 pages, 3 figures

Published
2021

182. A Novel Unified Framework for Solving Reachability, Viability and Invariance Problems

Author

Liao, Wei, Liang, Taotao, Wei, Xiaohui, and Lai, Jizhou

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The level set method is a widely used tool for solving reachability and invariance problems. However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal conditions for solving the Hamilton-Jacobi partial differential equation, limit the application of the level set method in some problems with non-affine nonlinear systems and irregular target sets. This paper proposes a method that can effectively avoid the above tricky issues and thus has better generality. In the proposed method, the reachable or invariant sets with different time horizons are characterized by some non-zero sublevel sets of a value function. This value function is not obtained by solving a viscosity solution of the partial differential equation but by recursion and interpolation approximation. At the end of this paper, some examples are taken to illustrate the accuracy and generality of the proposed method., Comment: arXiv admin note: text overlap with arXiv:2101.09646

Published
2021

185. Application of literature case presentation and discussion in teaching of clinical epidemiology for professional Master students

Author

HAN Huijun, LIAO Wei, WANG Li

Subjects
epidemiology, teaching methods, professional master of clinical medicine, case-based learning, research ability, Medicine
Abstract

Objective To strengthen the research capacity of professional Master students in clinical medicine,a new teaching mode of literature case presentation and discussion was tried in clinical epidemiology teaching and the teaching effectiveness was evaluated. Methods The professional Master students in clinical medicine of Peking Union Medical College who joined clinical epidemiology courses during 2019—2021 were selected as the research subjects(n=128). In 2019, the lecture-based teaching was adopted. In 2020 and 2021, a 4-hour literature case presentation and discussion class was added for the topic of “cohort study”. Feedback on the new teaching mode was collected through questionnaires from students taking the course in 2021. Results The average per-question score rate across all questions in the final examination for a student in 2020 was higher than that in 2019 (the median value was 84.7% vs 75.4%, P＜0.001). There was no statistical difference in the average per-question score rate across questions related to the cohort study topic for a student between the two years. Students′ score ratefor the case presentation and discussion in 2020 was positively correlated with the average per-question score rate for not only questions related to the cohort study topic but also all questions in the final examination(r=0.40, P＜0.01; r=0.31, P＜0.05). Among the students taking the course in 2021, 80% believed that they had achieved the knowledge-learning goal of this case discussion class. About 90% agreed that the teaching mode of literature case presentation and discussion was helpful in improving their ability in designing clinical research, reading literatures, presenting research work and carrying out team collaboration. All students were satisfied with this new teaching mode. Conclusions Conducting literature case presentation supplemented with discussion in clinical epidemiology teaching can help students better to Master clinical research methodology and enhance their comprehensive research capacity.

Published
2023
Full Text
View/download PDF

186. Multi-system infection caused by Cryptococcus gattii in a non-immunocompromised patient: a case report

Author

Liao Wei, Sun Long

Subjects
cryptococcus gattii, cryptococcal meningitis, cryptococcal pneumonia, immunity, infection, Medicine
Abstract

Cryptococcus is a dimorphic opportunistic pathogenic fungus. Cryptococcal infection mostly occurs in immunocompromised patients infected with HIV， organ transplantation， long-term use of cortisol hormone， chemotherapy， chronic leukemia and lymphoma， etc. Multi-system cryptococcal infection is rare in non-immunocompromised patients. In this article， one male adult case of Cryptococcus gattii meningitis complicated with Cryptococcus gattii pneumonia was reported. He had normal immune function and presented with meningitis as the initial manifestation and the respiratory system was involved. The patient underwent intrathecal injection + antifungal therapy via intravenous drip and ventriculoperitoneal shunt （VPS）. After corresponding interventions， he did not complain of fever， headache， cough， sputum and other discomforts. He received long-term oral intake of fluconazole + flucytosine after discharge， and was subject regular follow-up. The diagnosis and treatment of this case prompt that medical practitioners should consider the possibility that the population with normal immune function can also be infected with Cryptococcus in clinical practice. Both the nervous system and respiratory system can be infected. Besides conventional antifungal therapy via intravenous drip， intrathecal injection of antifungal drugs and VPS can also be considered in the treatment of Cryptococcal meningitis.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results