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  1. Article ; Online: Hazards and Improvement Measures of Microplastic Pollution

    Gan Quan / Tian Jinxiao / Li Zhixin / Mi Shiyu / Wang Wenmin

    E3S Web of Conferences, Vol 257, p

    A Review

    2021  Volume 03006

    Abstract: Microplastics is one category of plastics with relatively small diameter and is considered as the common ingredient of waste accumulation zone in oceans. However, since countless plastic products are emitted into oceans annually as waste all around the ... ...

    Abstract Microplastics is one category of plastics with relatively small diameter and is considered as the common ingredient of waste accumulation zone in oceans. However, since countless plastic products are emitted into oceans annually as waste all around the world, pollution caused by them is severe and the resulting problems have attracted attention globally, while current policies and cooperation around the globe for tackling microplastics pollution still need to be improved. To deal with microplatics-related problems in the ocean, our review first discussed the toxicity of microplastics based on previous research related to marine microplastics, which was caused by the plastics themselves and their leaching substances with impacts on marine creatures and human body along the food chain. After summarizing some measures that have been already performed, we suggested that the authority should take more actions to mitigate those problems resulted from microplastics, pay more attention on researching, and encourage citizens to offer their proposals. By finally analyzing the advantages and disadvantages of different handling methods, as well as physical, chemical, and biological treatment technologies on oceanic microplastic issues, our work provided experience on disposing microplastics waste under various actual situations with an example for more holistic waste treatment.
    Keywords Environmental sciences ; GE1-350
    Subject code 333
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher EDP Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: MDAN: Mirror Difference Aware Network for Brain Stroke Lesion Segmentation.

    Bao, Qiqi / Mi, Shiyu / Gang, Bowen / Yang, Wenming / Chen, Jie / Liao, Qingmin

    IEEE journal of biomedical and health informatics

    2022  Volume 26, Issue 4, Page(s) 1628–1639

    Abstract: Brain stroke lesion segmentation is of great importance for stroke rehabilitation neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of tissue intensity distribution, it remains a challenging task. To help detect ... ...

    Abstract Brain stroke lesion segmentation is of great importance for stroke rehabilitation neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of tissue intensity distribution, it remains a challenging task. To help detect abnormalities, the anatomical symmetries of brain magnetic resonance (MR) images have been widely used as visual cues for clinical practices. However, most methods for brain images segmentation do not fully utilize structural symmetry information. This paper presents a novel mirror difference aware network (MDAN) for stroke lesion segmentation. The network uses an encoder-decoder architecture, aiming at holistically exploiting the symmetries of image features. Specifically, a differential feature augmentation (DFA) module is developed in the encoding path to highlight the semantically pathological asymmetries of features in abnormalities. In the DFA module, a Siamese contrastive supervised loss is designed to enhance discriminative features, and a mirror position-based difference augmentation (MDA) module is used to further magnify the discrepancy. Moreover, mirror feature fusion (MFF) modules are applied to efficiently fuse and transfer the information both of the original input and the horizontally flipped features to the decoding path. Extensive experiments on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset show the proposed MDAN outperforms the state-of-the-art methods.
    MeSH term(s) Brain/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Stroke/diagnostic imaging
    Language English
    Publishing date 2022-04-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2021.3113460
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Detecting Carotid Intima-Media From Small-Sample Ultrasound Images.

    Mi, Shiyu / Wei, Zhanghong / Xu, Jinfeng / Yu, Zijun / Yang, Wenming / Liao, Qingmin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2020  Volume 2020, Page(s) 2129–2132

    Abstract: Cardiovascular diseases are the biggest threat to human being's health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great ... ...

    Abstract Cardiovascular diseases are the biggest threat to human being's health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great significance to detect the intima-media (IM). In this paper, a new IM detection method based on convolution neural network (IMD-CNN) is proposed for the detection of IM of blood vessels in longitudinal ultrasonic images. In IMD-CNN, firstly the region of interest (ROI) is automatically extracted by morphological processing, then the patch-wise training data are constructed, and finally a simple CNN is trained to detect the IM. The experimental results obtained on 23 images show that the test accuracy of IMD-CNN is over 86% and the performance of IMD-CNN is also visually proved to be effective.
    MeSH term(s) Carotid Intima-Media Thickness ; Communications Media ; Humans ; Neural Networks, Computer ; Plaque, Atherosclerotic/diagnostic imaging ; Ultrasonography
    Language English
    Publishing date 2020-10-05
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC44109.2020.9176282
    Database MEDical Literature Analysis and Retrieval System OnLINE

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