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您的检索式：作者名="Danny Z.Chen"
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| 1 | A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra显示文摘Objective and Impact Statement.Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination.In this work,we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues.Introduction.Raman spectra can reflect the substance components of the target tissues.However,the feature peak is slight and hard to detect due to environmental noise.Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches.Methods.First,we collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with the Raman shift ranging from 385 to 1545 cm^(−1).Second,a one-dimensional residual convolutional neural network(1D-ResNet)architecture is designed to classify the tumor tissues of colorectal cancer.Third,we visualize and interpret the fingerprint peaks found by our deep learning model.Results.Experimental results show that our deep learning method achieves 98.5%accuracy in the detection of colorectal cancer and outperforms traditional methods.Conclusion.Overall,Raman spectra are a novel modality for clinical detection of colorectal cancer.Our proposed ensemble 1D-ResNet could effectively classify the Raman spectra obtained from colorectal tumor tissues or normal tissues. | Zheng Cao Xiang Pan Hongyun Yu Shiyuan Hua Da Wang Danny Z.Chen Min Zhou Jian Wu | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
| 2 | AntVis:A web-based visual analytics tool for exploring ant movement data显示文摘We present AntVis,a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches.Our goal is to enable domain experts to visually explore massive ant movement data and gain valuable insights via effective visualization,filtering,and comparison.This is achieved through a deep learning framework for automatic detection,segmentation,and labeling of ants,ant movement clustering based on their trace similarity,and the design and development of five coordinated views(the movement,similarity,timeline,statistical,and attribute views)for user interaction and exploration.We demonstrate the effectiveness of AntVis with several case studies developed in close collaboration with domain experts.Finally,we report the expert evaluation conducted by an entomologist and point out future directions of this study. | Tianxiao Hu Hao Zheng Chen Liang Sirou Zhu Natalie Imirzian Yizhe Zhang Chaoli Wang David P.Hughes Danny Z.Chen | 2020 | Visual Informatics2020，4，1： | 0 |
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