The high-precision segmentation of retinal vessels in fundus images is important for the early diagnosis of ophthalmic diseases. However, the extraction for microvessels is challenging due to their characteristics of low contrast and high structural ...
Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Feb 5, 2024
Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analys...
IEEE journal of biomedical and health informatics
Feb 5, 2024
Medical image segmentation plays an important role in diagnosis. Since the introduction of U-Net, numerous advancements have been implemented to enhance its performance and expand its applicability. The advent of Transformers in computer vision has l...
IEEE journal of biomedical and health informatics
Feb 5, 2024
Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency ...
IEEE journal of biomedical and health informatics
Feb 5, 2024
Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care management, which offers the hope of revolutionary health care. An effective CIS method can help clinicians...
The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The ...
PURPOSE: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natu...
This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class a...
The U-shaped convolutional neural network (CNN) has attained remarkable achievements in the segmentation of skin lesion. However, given the inherent locality of convolution, this architecture cannot capture long-range pixel dependencies and multiscal...
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imagi...
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