AIMC Journal:
Medical image analysis
Showing 231 to 240 of 684 articles
Deep learning for computational cytology: A survey.
Medical image analysis
Nov 14, 2022
Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep le...
SD-CNN: A static-dynamic convolutional neural network for functional brain networks.
Medical image analysis
Nov 12, 2022
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology o...
From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping.
Medical image analysis
Nov 7, 2022
Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional...
Segmentation with mixed supervision: Confidence maximization helps knowledge distillation.
Medical image analysis
Nov 7, 2022
Despite achieving promising results in a breadth of medical image segmentation tasks, deep neural networks (DNNs) require large training datasets with pixel-wise annotations. Obtaining these curated datasets is a cumbersome process which limits the a...
Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation.
Medical image analysis
Nov 5, 2022
Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN i...
Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net.
Medical image analysis
Oct 29, 2022
The identification and quantification of liver lesions changes in longitudinal contrast enhanced CT (CECT) scans is required to evaluate disease status and to determine treatment efficacy in support of clinical decision-making. This paper describes a...
An explainable deep learning framework for characterizing and interpreting human brain states.
Medical image analysis
Oct 20, 2022
Deep learning approaches have been widely adopted in the medical image analysis field. However, a most of existing deep learning approaches focus on achieving promising performances such as classification, detection, and segmentation, and much less e...
Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation.
Medical image analysis
Oct 17, 2022
Semi-supervised learning has a great potential in medical image segmentation tasks with a few labeled data, but most of them only consider single-modal data. The excellent characteristics of multi-modal data can improve the performance of semi-superv...
Predictive uncertainty estimation for out-of-distribution detection in digital pathology.
Medical image analysis
Oct 17, 2022
Machine learning model deployment in clinical practice demands real-time risk assessment to identify situations in which the model is uncertain. Once deployed, models should be accurate for classes seen during training while providing informative est...
Semi-supervised body parsing and pose estimation for enhancing infant general movement assessment.
Medical image analysis
Oct 14, 2022
General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can...
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