AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Supervised Machine Learning

Showing 501 to 510 of 1604 articles

Clear Filters

Semi-Supervised Unpaired Medical Image Segmentation Through Task-Affinity Consistency.

IEEE transactions on medical imaging
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-super...

Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning.

Scientific reports
The demand for anomaly detection, which involves the identification of abnormal samples, has continued to increase in various domains. In particular, with increases in the data volume of medical imaging, the demand for automated screening systems has...

Local augmentation based consistency learning for semi-supervised pathology image classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Labeling pathology images is often costly and time-consuming, which is quite detrimental for supervised pathology image classification that relies heavily on sufficient labeled data during training. Exploring semi-supervised...

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading.

Virchows Archiv : an international journal of pathology
Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determine tumor size and Gleason score. This process is highly time-consuming, subjective, and relies on the...

Partial label learning: Taxonomy, analysis and outlook.

Neural networks : the official journal of the International Neural Network Society
Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the ...

Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model.

Environmental science and pollution research international
Differences in model application effectiveness, insufficient numbers of disaster samples, and unreasonable selection of non-hazard samples are common problems in landslide susceptibility studies. Therefore, in this paper, we propose a semi-integrated...

Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts.

BMC medical informatics and decision making
In recent years, relation extraction on unstructured texts has become an important task in medical research. However, relation extraction requires a large amount of labeled corpus, manually annotating sequences is time consuming and expensive. Theref...

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on...

Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging.

IEEE transactions on medical imaging
Non-rigid registration between 3D surfaces is an important but notorious problem in medical imaging, because finding correspondences between non-isometric instances is mathematically non-trivial. We propose a novel self-supervised method to learn sha...

Image processing and supervised machine learning for retinal microglia characterization in senescence.

Methods in cell biology
The process of senescence impairs the function of cells and can ultimately be a key factor in the development of disease. With an aging population, senescence-related diseases are increasing in prevalence. Therefore, understanding the mechanisms of c...