Browsing by Author "Huang, Jin"

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    A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis
    (Springer Nature, 2013) Huang, Heng; Yan, Jingwen; Nie, Feiping; Huang, Jin; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model.
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    High-throughput and high-accuracy diagnosis of multiple myeloma with multi-object detection
    (Optica Publishing Group, 2022-11-23) Mei, Liye; Shen, Hui; Yu, Yalan; Weng, Yueyun; Li, Xiaoxiao; Zahid, Kashif Rafiq; Huang, Jin; Wang, Du; Liu, Sheng; Zhou, Fuling; Lei, Cheng; Radiation Oncology, School of Medicine
    Multiple myeloma (MM) is a type of blood cancer where plasma cells abnormally multiply and crowd out regular blood cells in the bones. Automated analysis of bone marrow smear examination is considered promising to improve the performance and reduce the labor cost in MM diagnosis. To address the drawbacks in established methods, which mainly aim at identifying monoclonal plasma cells (monoclonal PCs) via binary classification, in this work, considering that monoclonal PCs is not the only basis in MM diagnosis, for the first we construct a multi-object detection model for MM diagnosis. The experimental results show that our model can handle the images at a throughput of 80 slides/s and identify six lineages of bone marrow cells with an average accuracy of 90.8%. This work makes a step further toward full-automatic and high-efficiency MM diagnosis.