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1,367 results on '"Zhu, Xiaohong"'

1. Multimodal imaging characteristics of Best vitelliform macular dystrophy

Author

Zhu Xiaohong, Zhao Yue, and Yao Jin

Subjects
best vitelliform macular dystrophy, retinal pigment epithelium, external segment of photoreceptor, choroidal neovascularization, optical coherence tomography angiography, imaging features, Ophthalmology, RE1-994
Abstract

AIM: To observe the multimodal imaging characteristics of Best vitelliform macular dystrophy(BVMD).METHODS:The clinical data of 30 patients(60 eyes)diagnosed as BVMD at stage Ⅰ to Ⅳ in Nanjing Medical University Affiliated Eye Hospital from June 2016 to October 2022 were collected for a retrospective analysis, and all patients are binocular involved. All patients underwent best corrected visual acuity(BCVA), slit lamp microscopy, indirect ophthalmoscopy, intraocular pressure, fundus photography, spectral-domain optical coherence tomography(SD-OCT), fundus autofluorescence(FAF), fundus fluorescein angiography(FFA), electro-oculogram(EOG)and optical coherence tomography angiography(OCTA).RESULTS: A total of 30 patients(60 eyes)were included, with 8 eyes at stage Ⅰ, 24 eyes at stage Ⅱ, 22 eyes at stage Ⅲ and 6 eyes at stage Ⅵ. The imaging characteristics of fundus photography, FAF, FFA and SD-OCT were basically consistent with previous literature reports. EOG showed Arden ratio

Published
2024
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8. Learning Incrementally to Segment Multiple Organs in a CT Image

Author

Liu, Pengbo, Wang, Xia, Fan, Mengsi, Pan, Hongli, Yin, Minmin, Zhu, Xiaohong, Du, Dandan, Zhao, Xiaoying, Xiao, Li, Ding, Lian, Wu, Xingwang, and Zhou, S. Kevin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

There exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to incrementally learn a multi-organ segmentation model. In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. While IL is notorious for its `catastrophic forgetting' weakness in the context of natural image analysis, we experimentally discover that such a weakness mostly disappears for CT multi-organ segmentation. To further stabilize the model performance across the IL stages, we introduce a light memory module and some loss functions to restrain the representation of different categories in feature space, aggregating feature representation of the same class and separating feature representation of different classes. Extensive experiments on five open-sourced datasets are conducted to illustrate the effectiveness of our method., Comment: arXiv admin note: text overlap with arXiv:2103.04526

Published
2022

35. A study on the prediction of development strategies of art education in universities based on deep learning model

Author

Zhu Xiaohong

Subjects
deep learning model, painting images, contextual encoder, image restoration network, performance prediction, 97d60, Mathematics, QA1-939
Abstract

In this paper, we propose a deep learning restoration method for fine art painting images based on a contextual encoder and select a convolutional self-encoder-based image restoration model for workflow analysis, including forward inference and backward network propagation. We design and develop a software system for predicting the grades of college art and painting students and apply the neural network prediction model to this system to realize the grade prediction of college art students. The accuracy of the GDPN model is 0.922, the precision is 0.904, and the recall is 0.966, which can consider both the temporal and overall information in the click behavior and achieve a better prediction effect.

Published
2024
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49. A substantial increase of Curie temperature in a new type of diluted magnetic semiconductors via effects of chemical pressure

Author

Yu, Shuang, Zhao, Guoqiang, Peng, Yi, Zhu, Xiaohong, Wang, Xiancheng, Zhao, Jianfa, Cao, Lipeng, Li, Wenmin, Li, Zhi, Deng, Zheng, and Jin, Changqing

Subjects
Condensed Matter - Materials Science, Condensed Matter - Strongly Correlated Electrons
Abstract

Chemical pressure is an effective method to tune physical properties, particularly for diluted magnetic semiconductors (DMS) of which ferromagnetic ordering is mediated by charge carriers. Via substitution of smaller Ca for larger Sr, we introduce chemical pressure on (Sr,Na)(Cd,Mn)2As2 to fabricate a new DMS material (Ca,Na)(Cd,Mn)2As2. Carriers and spins are introduced by substitutions of (Ca,Na) and (Cd,Mn) respectively. The unit cell volume reduces by 6.2% after complete substitution of Ca for Sr, suggesting a subsistent chemical pressure. Importantly the local geometry of [Cd/MnAs4] tetrahedron is optimized via chemical compression that increases the Mn-As hybridization leading to enhanced ferromagnetic interactions. As a result, the maximum Curie temperature (TC) is increased by about 50% while the the maximum saturation moment increases by over 100% from (Sr,Na)(Cd,Mn)2As2 to (Ca,Na)(Cd,Mn)2As2. The chemical pressure estimated from the equation of state is equal to an external physical pressure of 3.6 GPa., Comment: 19 pages, 4 figures

Published
2019
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