AIMC Journal:
IEEE transactions on medical imaging

Showing 171 to 180 of 687 articles

Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning.

IEEE transactions on medical imaging Aug 31, 2023
Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-learning algorithms have been developed for automatic retinal fundus image analysis. However, existing methods are usually data-driven, which rarely consider th...
Humans Algorithms Image Processing, Computer-Assisted Fundus Oculi Supervised Machine Learning Retinal Diseases
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Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs.

IEEE transactions on medical imaging Aug 31, 2023
Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a sma...
Neural Networks, Computer Image Processing, Computer-Assisted Semantics
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Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.

IEEE transactions on medical imaging Aug 31, 2023
Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed ...
Algorithms Computer Simulation Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Signal-To-Noise Ratio
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H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation.

IEEE transactions on medical imaging Aug 31, 2023
Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very impor...
Neural Networks, Computer Diagnosis, Computer-Assisted
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SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation.

IEEE transactions on medical imaging Aug 31, 2023
Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkab...
Neural Networks, Computer Image Processing, Computer-Assisted Supervised Machine Learning Retinal Vessels
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FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.

IEEE transactions on medical imaging Aug 1, 2023
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based o...
Humans Algorithms Female Breast Neoplasms Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Phantoms, Imaging
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Hierarchical Bias Mitigation for Semi-Supervised Medical Image Classification.

IEEE transactions on medical imaging Aug 1, 2023
Semi-supervised learning (SSL) has demonstrated remarkable advances on medical image classification, by harvesting beneficial knowledge from abundant unlabeled samples. The pseudo labeling dominates current SSL approaches, however, it suffers from in...
Bias Supervised Machine Learning Motion
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Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning.

IEEE transactions on medical imaging Aug 1, 2023
Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets wi...
Humans Machine Learning Deep Learning Child Image Processing, Computer-Assisted Leukemia
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Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images.

IEEE transactions on medical imaging Aug 1, 2023
CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and real-application dataset, i.e., methods trained on simulation cannot generalize well to practical data...
Computer Simulation Tomography, X-Ray Computed Deep Learning Artifacts
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Do Gradient Inversion Attacks Make Federated Learning Unsafe?

IEEE transactions on medical imaging Jun 30, 2023
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent...
Humans Neural Networks, Computer Supervised Machine Learning Privacy Medical Informatics
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