@article {327,
	title = {Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study},
	volume = {129},
	year = {2021},
	month = {2021/06/01},
	pages = {1837 - 1856},
	abstract = {<p>The characterization and classification of white blood cells (WBC) are critical for the diagnosis of anemia, leukemia, and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158\&nbsp; s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375\&nbsp; s). Our findings suggest that WBC-Profiler has the potential clinical implications.</p>
},
	isbn = {1573-1405},
	url = {https://doi.org/10.1007/s11263-021-01449-9},
	author = {Yan, Hong and Mao, Xuanyu and Yang, Xu and Xia, Yongquan and Wang, Chengbin and Wang, Junjun and Xia, Rui and Xu, Xuejing and Wang, Zhiqiang and Li, Zhiyang and Zhao, Xie and Li, Yan and Liu, Guoye and He, Li and Wang, Zhongyu and Wang, Zhiqiong and Li, Zhiqiang and Cai, Weidong and Shen, Han and Chang, Hang}
}
@article {331,
	title = {Distinct Clinical Impact and Biological Function of Angiopoietin and Angiopoietin-like Proteins in Human Breast Cancer.},
	journal = {Cells},
	volume = {10},
	year = {2021},
	month = {2021 09 29},
	abstract = {<p>Secreted angiopoietin/angiopoietin-like (/) proteins are involved in many biological processes. However, the role of these proteins in human breast cancers (BCs) remains largely unclear. Here, we conducted integrated omics analyses to evaluate the clinical impact of / proteins and to elucidate their biological functions. In BCs, we identified rare mutations in / genes, frequent gains of , , and , and frequent losses of , , and , but observed that , , and were robustly downregulated in multiple datasets. The expression levels of , , and were positively correlated with overall survival (OS), while the expression levels of were negatively correlated with OS. Additionally, the expression levels of and were positively correlated with distant metastasis-free survival (DMFS), while the expression levels of and were negatively correlated with DMFS. The prognostic impacts of / genes depended on the molecular subtypes and on clinical factors. We discovered that various / genes were co-expressed with various genes involved in different pathways. Finally, with the exception of , the remaining genes showed significant correlations with cancer-associated fibroblasts, endothelial cells, and microenvironment score, whereas only was significantly correlated with immune score. Our findings provide strong evidence for the distinct clinical impact and biological function of / proteins, but the question of whether some of them could be potential therapeutic targets still needs further investigation in BCs.</p>
},
	keywords = {Angiopoietin-like Proteins, Angiopoietins, Breast Neoplasms, Female, Humans, Prognosis},
	issn = {2073-4409},
	doi = {10.3390/cells10102590},
	author = {Yang, Hui and Zhang, Melody and Mao, Xuan-Yu and Chang, Hang and Perez-Losada, Jesus and Mao, Jian-Hua}
}
@article {89,
	title = {Diagnostic and prognostic histopathology system using morphometric indices},
	number = {13/886,213},
	year = {2015},
	month = {05/2015},
	chapter = {9031306},
	author = {Bahram Parvin and Hang Chang and Ju Han and Gerald Fontenay}
}
@conference {33,
	title = {Detection of 3D filamentous networks from tomographic electron microscopy},
	booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings},
	year = {2012},
	doi = {10.1109/ISBI.2012.6235826},
	url = {http://dx.doi.org/10.1109/ISBI.2012.6235826},
	author = {Leandro A. Loss and Hang Chang and Purbasha Sarkar and Manfred Auer and Bahram Parvin}
}
@conference {42,
	title = {A Delaunay Triangulation Approach for Segmenting Clumps of Nuclei},
	booktitle = {Proceedings of the 2009 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009},
	year = {2009},
	doi = {10.1109/ISBI.2009.5192970},
	url = {http://dx.doi.org/10.1109/ISBI.2009.5192970},
	author = {Quan Wen and Hang Chang and Bahram Parvin}
}
