AIMC Journal:
Medical image analysis

Showing 331 to 340 of 684 articles

Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.

Medical image analysis Aug 25, 2021
Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. However, the efficiency and accuracy of such a biomechanical kinematic analysis vary significantly am...
Humans Neural Networks, Computer Deep Learning Cervical Vertebrae Algorithms Machine Learning
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AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.

Medical image analysis Aug 6, 2021
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution ...
Cerebrovascular Circulation Magnetic Resonance Imaging Brain Ischemia Perfusion Perfusion Imaging Stroke Reproducibility of Results Humans Deep Learning
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MedQ: Lossless ultra-low-bit neural network quantization for medical image segmentation.

Medical image analysis Aug 2, 2021
Implementing deep convolutional neural networks (CNNs) with boolean arithmetic is ideal for eliminating the notoriously high computational expense of deep learning models. However, although lossless model compression via weight-only quantization has ...
Image Processing, Computer-Assisted Data Compression Neural Networks, Computer Humans
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A positive/unlabeled approach for the segmentation of medical sequences using point-wise supervision.

Medical image analysis Jul 31, 2021
The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To a...
Bayes Theorem Supervised Machine Learning Humans
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Deep reinforcement learning in medical imaging: A literature review.

Medical image analysis Jul 27, 2021
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks. Recent works have demonstrated the great po...
Diagnostic Imaging Algorithms Learning Forecasting Humans Neural Networks, Computer
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Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.

Medical image analysis Jul 24, 2021
Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supervision (e.g., pixel-level annotation) which is challenging to acquire. In this paper, we propose a ...
Neural Networks, Computer Image Processing, Computer-Assisted Humans
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Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.

Medical image analysis Jul 14, 2021
Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However, there are domains where the training of classification models that generalize on several datasets ...
Neoplasm Grading Supervised Machine Learning Prostatic Neoplasms Humans Male Neural Networks, Computer
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Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition.

Medical image analysis Jul 8, 2021
Surgical workflow recognition is a fundamental task in computer-assisted surgery and a key component of various applications in operating rooms. Existing deep learning models have achieved promising results for surgical workflow recognition, heavily ...
Neural Networks, Computer Supervised Machine Learning Workflow Reproducibility of Results Surgery, Computer-Assisted
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Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean.

Medical image analysis Jul 1, 2021
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled data...
Supervised Machine Learning Humans Radiology Radiography
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Self-paced and self-consistent co-training for semi-supervised image segmentation.

Medical image analysis Jun 26, 2021
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-trainin...
Supervised Machine Learning Neural Networks, Computer Humans Uncertainty Entropy Image Processing, Computer-Assisted
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