Search Results for author: Hong-Wen Deng

Found 5 papers, 0 papers with code

A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery

no code implementations13 Jan 2022 Md ashad Alam, Hui Shen, Hong-Wen Deng

Many statistical machine approaches could ultimately highlight novel features of the etiology of complex diseases by analyzing multi-omics data.

Causal Inference Drug Discovery

A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images

no code implementations3 Feb 2021 Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou

In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.

Interactive Segmentation

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

no code implementations25 Jan 2021 Chen Zhao, Haipeng Tang, Daniel McGonigle, Zhuo He, Chaoyang Zhang, Yu-Ping Wang, Hong-Wen Deng, Robert Bober, Weihua Zhou

We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.

A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

no code implementations9 Jun 2020 Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou

During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.

Semantic Segmentation

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets

no code implementations29 Apr 2020 Md. Ashad Alam, Chuan Qiu, Hui Shen, Yu-Ping Wang, Hong-Wen Deng

In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.

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