Your search for: 'author:("Liu, Junjun")' has returned 2 results. (0.009s) Save search

Authors: Zhang, Jun | Liu, Junjun

Article Type: Research Article

Abstract: Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote …sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness. Show more

Keywords: Cloud detection, remote sensing, convolutional neural network, kernel principal component analysis, saliency model, super-pixel segment

DOI: 10.3233/MGS-210352

Citation: Multiagent and Grid Systems, vol. 17, no. 3, pp. 235-247, 2021

Authors: Ji, Yu | Shao, Zhenzhen | Liu, Junjun | Hao, Yujuan | Liu, Peifang

Article Type: Research Article

Abstract: This study aimed to analyze the correlation between mammographic density obtained by density analysis software (DAS)/radiologists visual (RV) classification with molecular subtype, and the expression levels of estrogen receptor (ER), progesterone receptor (PR), Ki67 antigen (Ki-67), p53 gene (p53), and human epidermal growth factor receptor-2 (HER2). A total of 688 breast cancer patients with digital mammography and complete molecular pathological results in Tianjin Medical University Cancer Institute and Hospital between February 2015 and February 2016 were collected. The DAS-density grade (DASD) and the radiologists visually classified density grade (RVD) were evaluated by 3 radiologists. The correlation between density grade and …the expression levels of ER, PR, Ki-67, p53, HER2 and breast cancer molecular subtype (PMS) were analyzed. The agreement between DASD and RVD was explored. ER, PR and HER-2 positive rate were significantly different among patients with different RVD grades (P < 0.05). HER2 positive rates showed an increasing trend following RVD upgrading (P 𝑡𝑟𝑒𝑛𝑑 < 0.05). HER-2 positive rate in RVD D1 + D2 was 7.69%, which was higher than that in D3 + D4 (P < 0.05). The ER and Ki-67 expressions in patients were markedly different among DASD (P = 0.009 and 0.002) and RVD (P = 0.012 and 0.036) with different grades. The kappa value of each DASD to RVD was 0.31 (P < 0.01). The RVD 3 proportion was 14.58% (63/432) in HER2 Over-expressing subtype, which was apparently higher than RVD1 (2.43%, 1/41) (P < 0.05). Breast density may be partial correlated with molecular pathology in breast cancer. Show more

Keywords: Breast cancer, mammographic density, molecular pathology, correlation

DOI: 10.3233/CBM-181185

Citation: Cancer Biomarkers, vol. 22, no. 3, pp. 523-531, 2018