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OpenHI - An open source framework for annotating histopathological image CPCI-S
会议论文 | 2018 , 1076-1082 | IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Abstract :

Histopathological images carry informative cellular visual phenotypes and have been digitalized in huge amount in medical institutes. However, the lack of software for annotating the specialized images has been a hurdle of fully exploiting the images for educating and researching, and enabling intelligent systems for automatic diagnosis or phenotype-genotype association study. This paper proposes an open-source web framework, OpenHI, for the whole-slide image annotation. The proposed framework could be utilized for simultaneous collaborative or crowd-sourcing annotation with standardized semantic enrichment at a pixel-level precision. Meanwhile, our accurate virtual magnification indicator provides annotators a crucial reference for deciding the grading of each region. In testing, the framework can responsively annotate the acquired whole-slide images from TCGA project and provide efficient annotation which is precise and semantically meaningful. OpenHI is an open-source framework thus it can be extended to support the annotation of whole-slide images from different source with different oncological types. It is publicly available at https://gitlab.com/BioAI/OpenHI/. The framework may facilitate the creation of large-scale precisely annotated histopathological image datasets.

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GB/T 7714 Puttapirat, Pargorn , Zhang, Haichuan , Lian, Yuchen et al. OpenHI - An open source framework for annotating histopathological image [C] . 2018 : 1076-1082 .
MLA Puttapirat, Pargorn et al. "OpenHI - An open source framework for annotating histopathological image" . (2018) : 1076-1082 .
APA Puttapirat, Pargorn , Zhang, Haichuan , Lian, Yuchen , Wang, Chunbao , Zhang, Xingrong , Yao, Lixia et al. OpenHI - An open source framework for annotating histopathological image . (2018) : 1076-1082 .
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Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning EI SCIE Scopus
期刊论文 | 2018 , 10 (2) | REMOTE SENSING
WoS CC Cited Count: 11 SCOPUS Cited Count: 6
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Abstract :

Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature. However, the identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of endmembers in a given scene. To deal with these problems, the sparsity-based unmixing algorithms, which regard a large standard spectral library as endmembers, have recently been proposed. However, the high mutual coherence of spectral libraries always affects the performance of sparse unmixing. In addition, the hyperspectral image has the special characteristics of space. In this paper, a new unmixing algorithm via low-rank representation (LRR) based on space consistency constraint and spectral library pruning is proposed. The algorithm includes the spatial information on the LRR model by means of the spatial consistency regularizer which is based on the assumption that: it is very likely that two neighbouring pixels have similar fractional abundances for the same endmembers. The pruning strategy is based on the assumption that, if the abundance map of one material does not contain any large values, it is not a real endmember and will be removed from the spectral library. The algorithm not only can better capture the spatial structure of data but also can identify a subset of the spectral library. Thus, the algorithm can achieve a better unmixing result and improve the spectral unmixing accuracy significantly. Experimental results on both simulated and real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.

Keyword :

dictionary pruning space consistency constraint spectral unmixing hyperspectral remote sensing

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GB/T 7714 Zhang, Xiangrong , Li, Chen , Zhang, Jingyan et al. Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning [J]. | REMOTE SENSING , 2018 , 10 (2) .
MLA Zhang, Xiangrong et al. "Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning" . | REMOTE SENSING 10 . 2 (2018) .
APA Zhang, Xiangrong , Li, Chen , Zhang, Jingyan , Chen, Qimeng , Feng, Jie , Jiao, Licheng et al. Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning . | REMOTE SENSING , 2018 , 10 (2) .
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Improving chinese sentiment analysis via segmentation-based representation using parallel CNN EI Scopus
会议论文 | 2017 , 10604 LNAI , 668-680 | 13th International Conference on Advanced Data Mining and Applications, ADMA 2017
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Abstract :

Automatically analyzing sentimental implications in texts relies on well-designed models utilizing linguistic features. Therefore, the models are mostly language-dependent and designed for English texts. Chinese is with the largest users in the world and has a tremendous amount of texts daily generated from the social media, etc. However, it has seldom been studied. On another hand, a general observation, which is valid in many languages, is that different segments of a piece of text, e.g. a clause, having different sentimental polarities. The existing deep learning models neglect the imbalanced sentiment distribution and only take the entire piece of the text. This paper proposes a novel sentiment-analysis model, which is capable of sentiment analysis task in Chinese. Firstly, the model segments a text into smaller units according to the punctuations to obtain the preliminary text representation, and this step is so-called segmentation-based representation. Meanwhile, its new framework parallel-CNN (convolutional neural network) simultaneously use all segments. This model, we call SBR-PCNN, concatenate the representation of each segment to obtain the final representation of the text which does not only contain the semantic and syntactic features but also retains the essential sequential information. The proposed method has been evaluated on two Chinese sentiment classification datasets and compared with a broad range of baselines. Experimental results show that the proposed approach achieves the state of the art results on two benchmarking datasets. Meanwhile, they demonstrate that our model may improve the performance of Chinese sentiment analysis. © Springer International Publishing AG 2017.

Keyword :

Chinese Convolutional neural network Linguistic features Sentiment classification Sentiment distributions Sequential information Syntactic features Text representation

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GB/T 7714 Hao, Yazhou , Zheng, Qinghua , Lan, Yangyang et al. Improving chinese sentiment analysis via segmentation-based representation using parallel CNN [C] . 2017 : 668-680 .
MLA Hao, Yazhou et al. "Improving chinese sentiment analysis via segmentation-based representation using parallel CNN" . (2017) : 668-680 .
APA Hao, Yazhou , Zheng, Qinghua , Lan, Yangyang , Li, Yufei , Wang, Meng , Wang, Sen et al. Improving chinese sentiment analysis via segmentation-based representation using parallel CNN . (2017) : 668-680 .
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