no code implementations • CCL 2020 • Yuling Tang, Dong Yu
本文提出了可读性语料库构建的改进方法, 基于该方法, 构建了规模更大的汉语句子可读性语料库。该语料库在句子绝对难度评估任务上的准确率达到0. 7869, 相对前人工作提升了0. 15以上, 证明了改进方法的有效性。将深度学习方法应用于汉语可读性评估, 探究了不同深度学习方法自动捕获难度特征的能力, 并进仛步探究了向深度学习特征中融入不同层面的语难度特征对模型整体性能的影响。实验结果显示, 不同深度学习模型的难度特征捕获能力不尽相同, 语言难度特征可以不同程度地提高深度学习模型的难度表征能力。
no code implementations • 21 Aug 2020 • Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Yang Li, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Xiangjun Feng, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
no code implementations • 14 Mar 2018 • Zhang Li, Zheyu Hu, Jiaolong Xu, Tao Tan, Hui Chen, Zhi Duan, Ping Liu, Jun Tang, Guoping Cai, Quchang Ouyang, Yuling Tang, Geert Litjens, Qiang Li
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
no code implementations • 10 Feb 2018 • Tao Tan, Zhang Li, Haixia Liu, Ping Liu, Wenfang Tang, Hui Li, Yue Sun, Yusheng Yan, Keyu Li, Tao Xu, Shanshan Wan, Ke Lou, Jun Xu, Huiming Ying, Quchang Ouyang, Yuling Tang, Zheyu Hu, Qiang Li
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).