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UNETR: Transformers for 3D Medical Image Segmentation [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021 Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global
Ali Hatamizadeh +3 more
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Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [PDF]
Medical Image Analysis, 2023 The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface.
Junde Wu +7 more
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UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Huimin Huang +8 more
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Frontiers in Medicine, 2023 Background and purposeColorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide.
Liyu Shi +18 more
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UNet++: A Nested U-Net Architecture for Medical Image Segmentation [PDF]
DLMIA/ML-CDS@MICCAI, 2018 In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of ...
Zongwei Zhou +3 more
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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation [PDF]
Computer Vision and Pattern Recognition, 2023 In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and un-labeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data ...
Yunhao Bai +4 more
semanticscholar +1 more source
A survey on deep learning in medical image analysis [PDF]
Medical Image Anal., 2017 Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 ...
G. Litjens +8 more
semanticscholar +1 more source
Customized Segment Anything Model for Medical Image Segmentation [PDF]
arXiv.org, 2023 We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing ...
Kaiwen Zhang, Dong Liu
semanticscholar +1 more source
BMC Cardiovascular Disorders, 2023 Background This study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI).
Yongxiang Tang +11 more
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
Fausto Milletarì +2 more
semanticscholar +1 more source