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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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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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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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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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications [PDF]
IEEE Access, 2020 U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for ...
N. Siddique +3 more
semanticscholar +1 more source
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [PDF]
Computer Vision and Pattern Recognition, 2021 Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when ...
Quande Liu +4 more
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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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A Hybrid Technique for Medical Image Segmentation [PDF]
Journal of Biomedicine and Biotechnology, 2012 Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter.
Nyma, Alamgir +4 more
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MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023 There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging ...
Saikat Roy +7 more
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MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [PDF]
AAAI Conference on Artificial Intelligence, 2023 The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive ...
Junde Wu +4 more
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