AIMC Journal:
IEEE transactions on medical imaging

Showing 651 to 660 of 699 articles

FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis.

IEEE transactions on medical imaging Jun 1, 2025
Ultrasonography plays an essential role in breast cancer diagnosis. Current deep learning based studies train the models on either images or videos in a centralized learning manner, lacking consideration of joint benefits between two different modali...
Humans Female Breast Neoplasms Breast Video Recording Image Interpretation, Computer-Assisted Deep Learning Ultrasonography, Mammary
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GLIMPSE: Generalized Locality for Scalable and Robust CT.

IEEE transactions on medical imaging May 30, 2025
Deep learning has become the state-of-the-art approach to medical tomographic imaging. A common approach is to feed the result of a simple inversion, for example the backprojection, to a multiscale convolutional neural network (CNN) which computes th...
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Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoders.

IEEE transactions on medical imaging May 14, 2025
Masked Autoencoder (MAE) is a self-supervised pre-training technique that holds promise in improving the representation learning of neural networks. However, the current application of MAE directly to volumetric medical images poses two challenges: (...
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Disentangled Pseudo-bag Augmentation for Whole Slide Image Multiple Instance Learning.

IEEE transactions on medical imaging May 14, 2025
As the predominant approach for pathological whole slide image (WSI) classification, multiple instance learning (MIL) methods struggle with limited labeled WSIs. Although MIL has achieved notable progress with pseudo-bag-oriented augmentation methods...
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SA-Seg: Annotation-Efficient Segmentation for Airway Tree Using Saliency-based Annotation.

IEEE transactions on medical imaging May 12, 2025
Segmentation of the airway tree plays a vital role in clinical practice. However, the complex airway tree structure makes it quite challenging to annotate accurately. Although some annotation-efficient methods have shown promising results in medical ...
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Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging.

IEEE transactions on medical imaging Aug 1, 2024
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning...
Deep Learning Thorax Image Processing, Computer-Assisted Movement Heart Algorithms Artifacts Magnetic Resonance Imaging Adult Humans
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A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.

IEEE transactions on medical imaging Aug 1, 2020
Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-C...
Male Middle Aged Young Adult Adult Image Interpretation, Computer-Assisted Betacoronavirus Child Tomography, X-Ray Computed COVID-19 Thorax Aged Supervised Machine Learning Pneumonia, Viral Female Retrospective Studies Humans Aged, 80 and over Coronavirus Infections Lung SARS-CoV-2 Pandemics Adolescent
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Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.

IEEE transactions on medical imaging Aug 1, 2020
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in ca...
Humans Neural Networks, Computer COVID-19 Algorithms Betacoronavirus Pulmonary Disease, Chronic Obstructive Pandemics Pneumonia, Viral Lung Tomography, X-Ray Computed Coronavirus Infections SARS-CoV-2
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A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.

IEEE transactions on medical imaging Aug 1, 2020
Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to col...
Deep Learning Coronavirus Infections Pneumonia, Viral Pandemics Betacoronavirus SARS-CoV-2 Algorithms COVID-19 Lung Tomography, X-Ray Computed Humans
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A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.

IEEE transactions on medical imaging Aug 1, 2020
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid ...
Algorithms Tomography, X-Ray Computed Pandemics COVID-19 Deep Learning Coronavirus Infections Betacoronavirus SARS-CoV-2 Lung Humans Pneumonia, Viral
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