AIMC Journal:
IEEE transactions on medical imaging

Showing 211 to 220 of 687 articles

FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network.

IEEE transactions on medical imaging Apr 3, 2023
An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work...
Neural Networks, Computer ROC Curve
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Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework.

IEEE transactions on medical imaging Apr 3, 2023
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset o...
Neural Networks, Computer Supervised Machine Learning Cell Nucleus
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MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion.

IEEE transactions on medical imaging Apr 3, 2023
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denoising is largely studied both within the medical imaging community and beyond the community as a gener...
Humans Neural Networks, Computer Magnetic Resonance Imaging Artifacts
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MNAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

IEEE transactions on medical imaging Mar 2, 2023
Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent dia...
Neural Networks, Computer Tomography, X-Ray Computed Signal-To-Noise Ratio
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Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

IEEE transactions on medical imaging Mar 2, 2023
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva...
Humans Algorithms Brain Deep Learning Image Processing, Computer-Assisted Abdomen Abdominal Cavity
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Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

IEEE transactions on medical imaging Mar 2, 2023
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learni...
Humans Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Myocardium Heart Perfusion Myocardial Perfusion Imaging
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Semi-Supervised Unpaired Medical Image Segmentation Through Task-Affinity Consistency.

IEEE transactions on medical imaging Mar 2, 2023
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-super...
Algorithms Supervised Machine Learning
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Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.

IEEE transactions on medical imaging Feb 2, 2023
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or D...
Neural Networks, Computer Image Processing, Computer-Assisted Radiography Thorax X-Rays
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Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging.

IEEE transactions on medical imaging Feb 2, 2023
Non-rigid registration between 3D surfaces is an important but notorious problem in medical imaging, because finding correspondences between non-isometric instances is mathematically non-trivial. We propose a novel self-supervised method to learn sha...
Neural Networks, Computer Tomography, X-Ray Computed Supervised Machine Learning Bone and Bones
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Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

IEEE transactions on medical imaging Feb 2, 2023
Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imagi...
Neural Networks, Computer Computer Simulation Image Processing, Computer-Assisted Electrocardiography Heart
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