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2,333 results on '"Hu, Tingting"'

3. Image registration based automated lesion correspondence pipeline for longitudinal CT data

Author

Mukherjee, Subrata, Coroller, Thibaud, Wang, Craig, Samala, Ravi K., Hu, Tingting, Gokcay, Didem, Petrick, Nicholas, Sahiner, Berkman, and Cao, Qian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess these lesions to gain a comprehensive understanding of the disease's response to treatment. Computerized analysis methods that rely on lesion-level tracking have often used manual matching of corresponding lesions, a time-consuming process that is prone to errors. This paper introduces an automated lesion correspondence algorithm designed to precisely track both targets' lesions and non-targets' lesions in longitudinal data. Here we demonstrate the applicability of our algorithm on the anonymized data from two Phase III trials. The dataset contains imaging data of patients for different follow-up timepoints and the radiologist annotations for the patients enrolled in the trials. Target and non-target lesions are annotated by either one or two groups of radiologists. To facilitate accurate tracking, we have developed a registration-assisted lesion correspondence algorithm. The algorithm employs a sequential two-step pipeline: (a) Firstly, an adaptive Hungarian algorithm is used to establish correspondence among lesions within a single volumetric image series which have been annotated by multiple radiologists at a specific timepoint. (b) Secondly, after establishing correspondence and assigning unique names to the lesions, three-dimensional rigid registration is applied to various image series at the same timepoint. Registration is followed by ongoing lesion correspondence based on the adaptive Hungarian algorithm and updating lesion names for accurate tracking. Validation of our automated lesion correspondence algorithm is performed through triaxial plots based on axial, sagittal, and coronal views, confirming its efficacy in matching lesions.

Published
2024

5. Integrated image-based deep learning and language models for primary diabetes care

Author

Li, Jiajia, Guan, Zhouyu, Wang, Jing, Cheung, Carol Y., Zheng, Yingfeng, Lim, Lee-Ling, Lim, Cynthia Ciwei, Ruamviboonsuk, Paisan, Raman, Rajiv, Corsino, Leonor, Echouffo-Tcheugui, Justin B., Luk, Andrea O. Y., Chen, Li Jia, Sun, Xiaodong, Hamzah, Haslina, Wu, Qiang, Wang, Xiangning, Liu, Ruhan, Wang, Ya Xing, Chen, Tingli, Zhang, Xiao, Yang, Xiaolong, Yin, Jun, Wan, Jing, Du, Wei, Quek, Ten Cheer, Goh, Jocelyn Hui Lin, Yang, Dawei, Hu, Xiaoyan, Nguyen, Truong X., Szeto, Simon K. H., Chotcomwongse, Peranut, Malek, Rachid, Normatova, Nargiza, Ibragimova, Nilufar, Srinivasan, Ramyaa, Zhong, Pingting, Huang, Wenyong, Deng, Chenxin, Ruan, Lei, Zhang, Cuntai, Zhang, Chenxi, Zhou, Yan, Wu, Chan, Dai, Rongping, Koh, Sky Wei Chee, Abdullah, Adina, Hee, Nicholas Ken Yoong, Tan, Hong Chang, Liew, Zhong Hong, Tien, Carolyn Shan-Yeu, Kao, Shih Ling, Lim, Amanda Yuan Ling, Mok, Shao Feng, Sun, Lina, Gu, Jing, Wu, Liang, Li, Tingyao, Cheng, Di, Wang, Zheyuan, Qin, Yiming, Dai, Ling, Meng, Ziyao, Shu, Jia, Lu, Yuwei, Jiang, Nan, Hu, Tingting, Huang, Shan, Huang, Gengyou, Yu, Shujie, Liu, Dan, Ma, Weizhi, Guo, Minyi, Guan, Xinping, Yang, Xiaokang, Bascaran, Covadonga, Cleland, Charles R., Bao, Yuqian, Ekinci, Elif I., Jenkins, Alicia, Chan, Juliana C. N., Bee, Yong Mong, Sivaprasad, Sobha, Shaw, Jonathan E., Simó, Rafael, Keane, Pearse A., Cheng, Ching-Yu, Tan, Gavin Siew Wei, Jia, Weiping, Tham, Yih-Chung, Li, Huating, Sheng, Bin, and Wong, Tien Yin

Published
2024
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6. MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.

Author

Drukker, Karen, Sahiner, Berkman, Hu, Tingting, Kim, Grace Hyun, Whitney, Heather M, Baughan, Natalie, Myers, Kyle J, Giger, Maryellen L, and McNitt-Gray, Michael

Subjects
artificial intelligence, computer-aided diagnosis, machine learning, performance evaluation, Clinical sciences, Biomedical engineering
Abstract

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Published
2024

7. Ductal lavage followed by observation versus oral corticosteroids in idiopathic granulomatous mastitis: A randomized trial

Author

Chen, Xiaolin, Huang, Heng, Huang, Hui, Yong, Juanjuan, Zhu, Liling, Chen, Qianru, Tan, Luyuan, Zeng, Yinduo, Yang, Yaping, Zhao, Jianli, Rao, Nanyan, Ding, Linxiaoxiao, Wu, Wenjing, Li, Yudong, Gui, Xiujuan, Ye, Liming, Xu, Yanlian, Jiang, Yumei, Su, Linhong, Xiao, Qiaozhen, Cai, Xueying, Hu, Tingting, Tan, Cui, Liu, Qiongmei, Liu, Shuyi, Zhao, Jinghua, Wang, Ying, Yu, Fengyan, Zhang, Jian, Li, Shunrong, and Chen, Kai

Published
2024
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22. Reliability Analysis of Tram Electro-mechanical Braking System Based on Fuzzy Fault Tree

Author

MIAO Feng, WANG Cong, MAO Jinglu, YAO Yixiao, and HU Tingting

Subjects
tram, electro-mechanical braking, reliability, fuzzy fault tree analysis, Transportation engineering, TA1001-1280
Abstract

Objective The electro-mechanical braking system architecture of tram is different from that of the traditional braking system, so it is necessary to analyze the system safety and make targeted improvement to its weak links. Method Using fuzzy fault tree method can obtain the triangular fuzzy number of the top event occurrence probability through analysis of the test data, and then obtain the fuzzy importance of each bottom event. According to the fuzzy importance of the bottom events, the weak links of the system are analyzed and improved to realize the fault prevention. Result & Conclusion By analyzing the reliability of the tram electro-mechanical braking system based on fuzzy fault tree, the safety of the system weak links can be improved, and the corresponding improvement measures be put forward, helpful for improving the safety and reliability of the electro-mechanical braking system.

Published
2024
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