L
Li Liu
Researcher at National University of Defense Technology
Publications - 621
Citations - 19494
Li Liu is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 53, co-authored 411 publications receiving 11986 citations. Previous affiliations of Li Liu include Harvard University & University of the Sciences.
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Journal ArticleDOI
Intrapleural infusion of tumor cell-derived microparticles packaging methotrexate or saline combined with pemetrexed-cisplatin chemotherapy for the treatment of malignant pleural effusion in advanced non-squamous non-small cell lung cancer: A double-blind, randomized, placebo-controlled study
Xiaorong Dong,Yu Huang,Tienan Yi,Chunhong Hu,Quanli Gao,Yuan Chen,Jing Zhang,Jianhua Chen,Li Liu,Rui Meng,Shuang Zhang,Xiaofang Dai,Shi-Jiang Fei,Yang Jin,Ping Yin,Yanping Hu,Gang Wu +16 more
TL;DR: Intrapleural infusion of TMPs-MTX combined with pemetrexed-cisplatin chemotherapy is safe and effective against MPE in patients with advanced non-squamous NSCLC.
Journal ArticleDOI
Metastatic adenocarcinoma to the breast from the lung simulates primary breast carcinoma—a clinicopathologic study
TL;DR: Metastatic lung adenocarcinoma to the breast, although rare, should be considered in the differential diagnosis of primary breast carcinoma, especially when the breast lesion exhibits as a “triple-negative invasive carcinoma”.
Proceedings ArticleDOI
Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning
TL;DR: An end-to-end trainable tracking framework based on Siamese network is designed, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with the aim of mutual benefit.
Journal ArticleDOI
Large-range tunable fractional-order differentiator based on cascaded microring resonators
TL;DR: In this paper, an all-optical continuously tunable fractional-order differentiator using on-chip cascaded electrically tuned microring resonators (MRRs) was demonstrated.
Journal ArticleDOI
Random projections and Single BoW for fast and Robust texture segmentation
TL;DR: An approach to optimally learn new compact single BoW histogram models from the entire training set, with the single histogram providing benefits of efficiency in both memory and computation costs and the surprising conclusion that sparse reconstruction methods actually do not improve segmentation performance is demonstrated.