arXiv subjects

Yanting Li

Publications and source records attributed to Yanting Li.

25 records · Page 2Linked to original sources

arXiv2023

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC.

eess.IV↗

arXiv2023

A Directional Monitoring Approach of Sequential Incomplete Wind Power Curves with Copula-based Variational Inference

Wind turbines often work under complex conditions which result in performance degradation. Accurate performance degradation monitoring is essential to ensure the reliable operation of wind turbines and reduce the maintenance costs. Wind turbine power curve monitoring is an effective way to detect performance degradation. However, due to the intermittency and fluctuation of wind speed, the wind speed range varies at different time periods, making power curves difficult to compare. Motivated by this, we proposed copula-based variational inference framework and used it to establish a sequential incomplete wind power curve estimation algorithm. First, a monotone power curve is constructed based on copula-based variational inference and integrated spline regression model. Besides, the prior distribution of model parameters are sequentially updated. Then, a directional control chart based on a new statistic named KLdivergence factor is constructed to monitor wind turbine performance degradation. The real data of a wind farm in the east of the United Kingdom shows that the proposed method can both improve the accuracy of wind turbine power curve modeling and monitor wind turbine performance degradation more precisely and comprehensively than the existing approaches.

stat.AP↗

arXiv2023

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.

eess.IV↗

arXiv2022

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

Background: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters. Methods: A segmentation architecture that integrates a three-dimensional (3D) V-Net with a shape deformation module was developed. Using the shape priors generated by a dynamic programming (DP) algorithm, the model output was then constrained and guided during the model training for quick convergence and improved performance. A stratified 5-fold cross-validation was used to train and validate our models. Results: Results of our proposed method agree well with those from the ground truth. Our proposed model achieved a Dice similarity coefficient (DSC) of 0.9573(0.0244), 0.9821(0.0137), and 0.9903(0.0041), a Hausdorff distances (HD) of 6.7529(2.7334) mm, 7.2507(3.1952) mm, and 7.6121(3.0134) mm in extracting the endocardium, myocardium, and epicardium, respectively. Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.

eess.IV↗

arXiv2021

Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference

Multilingual transformers (XLM, mT5) have been shown to have remarkable transfer skills in zero-shot settings. Most transfer studies, however, rely on automatically translated resources (XNLI, XQuAD), making it hard to discern the particular linguistic knowledge that is being transferred, and the role of expert annotated monolingual datasets when developing task-specific models. We investigate the cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference (NLI), with a focus on the recent large-scale Chinese dataset OCNLI. To better understand linguistic transfer, we created 4 categories of challenge and adversarial tasks (totaling 17 new datasets) for Chinese that build on several well-known resources for English (e.g., HANS, NLI stress-tests). We find that cross-lingual models trained on English NLI do transfer well across our Chinese tasks (e.g., in 3/4 of our challenge categories, they perform as well/better than the best monolingual models, even on 3/5 uniquely Chinese linguistic phenomena such as idioms, pro drop). These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh). For many phenomena, all models continue to struggle, highlighting the need for our new diagnostics to help benchmark Chinese and cross-lingual models. All new datasets/code are released at https://github.com/huhailinguist/ChineseNLIProbing.

cs.CL↗

arXiv2020

CLUE: A Chinese Language Understanding Evaluation Benchmark

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.CLUEbenchmarks.com

cs.CL↗

arXiv2020

Energy Levels, Transition Rates and Electron Impact Excitation Rates for B-like Kr XXXII

Energy levels and transition rates for electric-dipole, electric-quadrupole, electric-octupole, magnetic-dipole, and magnetic-quadrupole transitions among the levels arising from the $n\ \leq$ 5 configurations in B-like Kr XXXII are calculated by using two state-of-the-art methods, namely, the multi-configuration Dirac-Hartree-Fock (MCDHF) approach and the second-order many-body perturbation theory (RMBPT). Our results are compared with several available experimental and other theoretical values. Electron-impact excitation (EIE) collision strengths are calculated via the independent process and isolated resonance approximation using distorted-wave (denoted by IPIRDW). Radiation damping effects on the resonance excitation contributions are included. Effective collision strengths are calculated as a function of electron temperature by assuming a Maxwellian electron velocity distribution. Spectral line intensities are modeled by using collision radiative model, and several line pairs pointed out might be useful for density diagnostics.

physics.atom-ph↗