Ms. Xiaomei Zhu Profile

Xiaomei Zhu

at Shenzhen Univ

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SPIE Journal Paper | 28 August 2021

Mask-based generative adversarial networking for crowd counting

Guoxiu Duan, Aichun Zhu, Lu Zhao, Xiaomei Zhu, Fangqiang Hu, Xinjie Guan

JEI, Vol. 30, Issue 04, 043027, (August 2021) https://doi.org/10.1117/12.10.1117/1.JEI.30.4.043027

KEYWORDS: Convolution, Gallium nitride, Head, Performance modeling, Neural networks, Network architectures, Image quality, Lithium, Image segmentation, Feature extraction

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Crowd counting is still a challenging task due to the variability of the distance scale, crowd occlusion, and complex background information. However, the deep convolution neural network has been proved to be effective in solving these problems. By loading input images, the network generates predicted density maps, and the average absolute error between the predicted density maps and given ground truth (GT) maps is a solid standard for evaluating the quality of the network. We propose a mask-based generative adversarial network (MBGAN) structure to generate accurate predicted density maps. The network consists of two parts: the generator and the discriminator. In the generator, we embed a fundamental feature extracting module, multiple level dilated convolution blocks, a predicted mask, and shortcut connection operations. The discriminator is mainly used to distinguish whether the density map comes from the generator or the GT and urges the generator to produce the density map that can confuse itself. The training of the proposed MBGAN model is through the joint action of density loss and adversarial loss. In the training strategy, we use the cross training of the generator and discriminator. Through experiments on five available datasets, the MBGAN achieved state-of-the-art performances that outperform other advanced methods.

Proceedings Article | 17 September 2014 Paper

The application of quantum dots for the melanoma tumor in vivo imaging

Yayi Feng, Peng Zhai, Xiaomei Wang, Ming Ying, Jinbo Wu, Xiaomei Zhu, Guimiao Lin, Qiang Chen, Gaixia Xu

Proceedings Volume 9230, 92301B (2014) https://doi.org/10.1117/12.2069011

KEYWORDS: Tumors, Melanoma, In vivo imaging, Quantum dots, Luminescence, Tissues, Blood vessels, Skin, Confocal microscopy, Cancer

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Objective: Over the past decade, fluorescent semiconductor nanocrystals, also known as quantum dots (QDs), have been applied in biomedical imaging in vitro and in vivo because of their fascinating optical properties. In this work, we investigated the application of CdTe QDs for tumor fluorescence in vivo imaging. Methods: The transparent dorsal skin fold window chamber (DSFC) was constructed on the 4~6 week-old BALB/c mice. The melanoma cells stably expressing green fluorescent protein ---ZsGreen were transplanted into the chamber and the melanoma DSFC model was established successfully. The water soluble CdTe QDs were synthesized and then administrated in the model through the tail intravenous injection. The fluorescent expression of B16 cells were assayed by fluorescent microscopy, the tumor growth, the blood capillaries distributions and its dynamic changes were observed by stereomicroscopy and laser scanning confocal microscopy. Results: The results demonstrated that the expression efficiency of ZsGreen was 41%, which met the experimental requirement. The tumors was visible inside the chamber after implantation of melanoma cells for 5~6 days, while no obvious changes in mice behaviors were found. After injection of the QDs, CdTe QDs accumulated at the invading edge of a range of solid tumor. We could also observe the tumor cells growth near the blood vessels, the angiogenesis occurred inside the tumor and the local blood capillaries increased. Conclusions: This work provided a new strategy for the tumor in vivo imaging and the development of targeted antineoplastic drugs.

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