AIMC Journal:
IEEE transactions on medical imaging

Showing 191 to 200 of 687 articles

Context Label Learning: Improving Background Class Representations in Semantic Segmentation.

IEEE transactions on medical imaging Jun 1, 2023
Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensi...
Neural Networks, Computer Image Processing, Computer-Assisted Semantics
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Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI.

IEEE transactions on medical imaging Jun 1, 2023
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techniques have been proposed to reduce the acquisition time, compressed sen...
Humans Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Artifacts Visual Perception
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Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

IEEE transactions on medical imaging Jun 1, 2023
In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnos...
Neural Networks, Computer Machine Learning Tomography, X-Ray Computed Diagnosis, Computer-Assisted ROC Curve
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Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation.

IEEE transactions on medical imaging Jun 1, 2023
Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised d...
Neural Networks, Computer Image Processing, Computer-Assisted Heart
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Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations.

IEEE transactions on medical imaging Jun 1, 2023
Convolutional neural networks (CNNs) have made enormous progress in medical image segmentation. The learning of CNNs is dependent on a large amount of training data with fine annotations. The workload of data labeling can be significantly relieved vi...
Neural Networks, Computer Reproducibility of Results Image Processing, Computer-Assisted Distillation
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Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps.

IEEE transactions on medical imaging Jun 1, 2023
Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits bot...
Algorithms Brain Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting.

IEEE transactions on medical imaging Jun 1, 2023
Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to...
Humans Software Supervised Machine Learning Calibration Monte Carlo Method
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Noise Suppression With Similarity-Based Self-Supervised Deep Learning.

IEEE transactions on medical imaging Jun 1, 2023
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but ...
Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Signal-To-Noise Ratio Photons
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Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning.

IEEE transactions on medical imaging May 2, 2023
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-traini...
Neural Networks, Computer Diagnosis, Computer-Assisted Ultrasonography
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Cancer Survival Prediction From Whole Slide Images With Self-Supervised Learning and Slide Consistency.

IEEE transactions on medical imaging May 2, 2023
Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard for cancer analysis and prognosis. Due to the scarcity of pixel- or patch-level annotations of WSIs, many existing methods attempt to predict survival outcomes...
Humans Neoplasms Supervised Machine Learning
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