quantification-circulating-pig-specific-dna-blood-xenotransplantation

Quantification of Circulating Pig-Specific DNA in the Blood of a Xenotransplantation Model

Xenotransplantation is a feasible method to treat organ failure. However, how to effectively monitor the immune rejection of xenotransplantation is a ...

Adult Pig Islet Isolation

Type 1 diabetes mellitus (T1DM) is caused by autoimmune destruction of pancreatic &#946; cells, which results in little or no insulin production. Islet ...

Shenzhen University Health Science Center, Shenzhen University School of Medicine, First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital

The First affiliated hospital of Shenzhen University, Shenzhen Second People's Hospital

With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification methods conduct their experiments on only one public dataset. When applying a well-trained model to a different dataset selected from different sources, the model usually shows large performance degradation and needs to be fine-tuned before it can be applied to the new dataset. The goal of this work is trying to solve the cross-domain image classification problem without using data from target domain. In this work, we designed a self-supervised plug-and-play feature-standardization-block (FSB) which consisting of image normalization (INB), contrast enhancement (CEB) and boundary detection blocks (BDB), to extract cross-domain robust feature maps for deep learning framework, and applied the network for chest x-ray-based lung diseases classification. Three classic deep networks, i.e. VGG, Xception and DenseNet and four chest x-ray lung diseases datasets were employed for evaluating the performance. The experimental result showed that when employing feature-standardization-block, all three networks showed better domain adaption performance. The image normalization, contrast enhancement and boundary detection blocks achieved in average 2%, 2% and 5% accuracy improvement, respectively. By combining all three blocks, feature-standardization-block achieved in average 6% accuracy improvement.

ArticleTotal : 2	Year
Publication title Cited by 2	2020
Publication title Cited by 2	2021

Zuhui Pu

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