AI Medical Compendium Topic:
Supervised Machine Learning

Clear Filters Showing 571 to 580 of 1605 articles

Multi-Agent Multi-View Collaborative Perception Based on Semi-Supervised Online Evolutive Learning.

Sensors (Basel, Switzerland)
In the edge intelligence environment, multiple sensing devices perceive and recognize the current scene in real time to provide specific user services. However, the generalizability of the fixed recognition model will gradually weaken due to the time...

Cooperative learning for multiview analysis.

Proceedings of the National Academy of Sciences of the United States of America
We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, proteomics, and radiomics, are measured on a common se...

Weakly Semi-supervised phenotyping using Electronic Health records.

Journal of biomedical informatics
OBJECTIVE: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR...

Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning-based methods for fast target segmentation of magnetic resonance imaging (MRI) have become increasingly popular in recent years. Generally, the success of deep learning methods in medical image segmentation tas...

Joint Label Inference and Discriminant Embedding.

IEEE transactions on neural networks and learning systems
Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requ...

Semi-supervised classifier guided by discriminator.

Scientific reports
Some machine learning applications do not allow for data augmentation or are applied to modalities where the augmentation is difficult to define. Our study aimed to develop a new method in semi-supervised learning (SSL) applicable to various modaliti...

Enhancing MR image segmentation with realistic adversarial data augmentation.

Medical image analysis
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impra...

Polyp segmentation with consistency training and continuous update of pseudo-label.

Scientific reports
Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervis...

Predicting genes associated with RNA methylation pathways using machine learning.

Communications biology
RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data...

Uncertainty-aware deep co-training for semi-supervised medical image segmentation.

Computers in biology and medicine
Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability...