AIMC Journal:
Medical image analysis

Showing 271 to 280 of 684 articles

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images.

Medical image analysis Jun 17, 2022
Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised ...
Deep Learning Supervised Machine Learning Humans Image Processing, Computer-Assisted
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Learning disentangled representations in the imaging domain.

Medical image analysis Jun 17, 2022
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts ...
Software Learning Humans Machine Learning
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Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency.

Medical image analysis Jun 15, 2022
Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. Semi-supervised...
Uncertainty Image Processing, Computer-Assisted Supervised Machine Learning Neural Networks, Computer Humans
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Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.

Medical image analysis Jun 11, 2022
Growing number of methods for attenuation-coefficient map estimation from magnetic resonance (MR) images have recently been proposed because of the increasing interest in MR-guided radiotherapy and the introduction of positron emission tomography (PE...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Deep Learning Tomography, X-Ray Computed Radiotherapy, Image-Guided Humans
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Deep learning models of cognitive processes constrained by human brain connectomes.

Medical image analysis Jun 7, 2022
Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity ...
Humans Magnetic Resonance Imaging Brain Cognition Neural Networks, Computer Connectome Deep Learning
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Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer.

Medical image analysis Jun 7, 2022
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squ...
Humans Algorithms Pancreatic Neoplasms Contrast Media Deep Learning Magnetic Resonance Imaging
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MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images.

Medical image analysis Jun 5, 2022
Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. Howeve...
Magnetic Resonance Imaging Deep Learning Tomography, X-Ray Computed Humans
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⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning.

Medical image analysis Jun 2, 2022
Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, e.g., to reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) acquisitions or estimate subject motion during an examination. MRI is natura...
Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Deep Learning Neural Networks, Computer
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Robust deep learning-based semantic organ segmentation in hyperspectral images.

Medical image analysis May 27, 2022
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic seg...
Image Processing, Computer-Assisted Semantics Deep Learning Neural Networks, Computer Swine Animals
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Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization.

Medical image analysis May 25, 2022
In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require accurate pixel-level annotations that are both time-consuming and laborious to generate. Typical app...
Algorithms Humans Supervised Machine Learning
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