Results 11 to 20 of about 340,403 (177)
Complexity, 2020 Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery.
Feng-Ping An, Jun-e Liu
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Trends and Techniques in Medical Image Segmentation for Disease Detection [PDF]
ITM Web of ConferencesMedical images have become an indispensable and important tool for the diagnosis of medical conditions and surgical guidance. As computer vision technology advances, Medical image segmentation technology has effectively assisted clinicians in making ...
Jiang Xinli
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The semiotics of medical image Segmentation [PDF]
Medical Image Analysis, 2018 As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive ...
Baxter, John S.H. +3 more
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A medical image segmentation method based on multi-dimensional statistical features
Frontiers in Neuroscience, 2022 Medical image segmentation has important auxiliary significance for clinical diagnosis and treatment. Most of existing medical image segmentation solutions adopt convolutional neural networks (CNNs).
Yang Xu +9 more
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Medical Image Segmentation Using Transformer Networks [PDF]
IEEE Access, 2022 Deep learning models represent the state of the art in medical image segmentation. Most of these models are fully-convolutional networks (FCNs), namely each layer processes the output of the preceding layer with convolution operations. The convolution operation enjoys several important properties such as sparse interactions, parameter sharing, and ...
Davood Karimi, Haoran Dou, Ali Gholipour
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Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey
Diagnostics, 2022 There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field.
Sheng-Yao Huang +3 more
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Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology [PDF]
, 2012 The incidence of thyroid nodule is very high and generally increases with the age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid nodule can be completely cured if detected early.
Tian, Hua +3 more
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Neutrosophic DICOM Image Processing and its applications [PDF]
Neutrosophic Sets and Systems, 2023 Medical images are essential in contemporary medicine because they provide practicable entropy, which is used to diagnose medical conditions. It is useful to visualize abnormality in several parts of the body.
D. Nagarajan, S. Broumi
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Impact of adversarial examples on deep learning models for biomedical image segmentation [PDF]
, 2019 Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples.
C Pena-Betancor +3 more
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Review of U-Net-Based Convolutional Neural Networks for Breast Medical Image Segmentation [PDF]
Jisuanji kexue yu tansuoU-Net and its variants have showcased exceptional performance in the domain of breast medical image segmentation. By employing a fully convolutional network (FCN) structure for semantic segmentation, the symmetrical structure of U-Net offers remarkable ...
PU Qiumei, YIN Shuai, LI Zhengmao, ZHAO Lina
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