A novel kernel Wasserstein distance on Gaussian measures: An application of identifying dental artifacts in head and neck computed tomography

HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network

Generative adversarial network (GAN) has achieved remarkable success in generating high-quality synthetic data by learning the underlying distributions of target data. Recent efforts have been devoted to utilizing optimal transport (OT) to tackle the gradient vanishing and instability issues in GAN. They use the Wasserstein distance as a metric to measure the discrepancy between the generator distribution and the real data distribution. However, most optimal transport GANs define loss functions in Euclidean space, which limits their capability in handling high-order statistics that are of much interest in a variety of practical applications. In this article, we propose a computational framework to alleviate this issue from both theoretical and practical perspectives. Particularly, we generalize the optimal transport-based GAN from Euclidean space to the reproducing kernel Hilbert space (RKHS) and propose Hilbert Optimal Transport GAN (HOT-GAN). First, we design HOT-GAN with a Hilbert embedding that allows the discriminator to tackle more informative and high-order statistics in RKHS. Second, we prove that HOT-GAN has a closed-form kernel reformulation in RKHS that can achieve a tractable objective under the GAN framework. Third, HOT-GAN's objective enjoys the theoretical guarantee of differentiability with respect to generator parameters, which is beneficial to learn powerful generators via adversarial kernel learning. Extensive experiments are conducted, showing that our proposed HOT-GAN consistently outperforms the representative GAN works.

Evaluation of land resources carrying capacity based on entropy weight and cloud similarity

Land is the foundation of human life and development, which is also the most important part of a country. The study of land carrying capacity is one of the important contents of land management, wherein the evaluation of land resource carrying capacity (LRCC) is an important reference for land resource planning. Aiming at the information fuzziness and uncertainty in the evaluation of LRCC, firstly, a comprehensive evaluation model based on entropy weight and normal cloud similarity was proposed, which is based on cloud model theory and combined with normal cloud similarity measurement method and entropy weight method. Secondly, taking the asphalt pavement experiment as an example for empirical analysis, the experimental results are consistent with the actual situation, which proves the feasibility and effectiveness of the proposed model. Finally, taking China's Chongqing city as the research area, the proposed evaluation model is used to study LRCC. The research results indicate that the comprehensive carrying capacity and average carrying capacity of various systems in China's Chongqing have been improved in the past decade. Among them, the comprehensive carrying capacity rose from the second level during the "12th Five-Year Plan" period to the third level during the "13th Five-Year Plan" period. In the future, it is necessary to focus on the improvement of soil and water resources system and economic and technological system. This conclusion reflects LRCC of Chongqing in China objectively and has a reference value for Chongqing's land planning.

Research on linguistic multi-attribute decision making method for normal cloud similarity

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.

Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering

Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k -means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k -means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results.