AI Medical Compendium Topic

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

Datasets as Topic

Showing 91 to 100 of 1078 articles

Clear Filters

Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contra...

Multi-Label Local to Global Learning: A Novel Learning Paradigm for Chest X-Ray Abnormality Classification.

IEEE journal of biomedical and health informatics
Deep neural network (DNN) approaches have shown remarkable progress in automatic Chest X-rays classification. However, existing methods use a training scheme that simultaneously trains all abnormalities without considering their learning priority. In...

Parasitic egg recognition using convolution and attention network.

Scientific reports
Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Ov...

Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis.

Journal of breath research
Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose diseas...

Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy.

Scientific reports
This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced usi...

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform.

Scientific reports
We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing t...

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018.

BMJ open
PURPOSE: In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the ...

Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios.

Physical and engineering sciences in medicine
This study aimed to investigate the robustness of a deep learning (DL) fusion model for low training-to-test ratio (TTR) datasets in the segmentation of gross tumor volumes (GTVs) in three-dimensional planning computed tomography (CT) images for lung...

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

IEEE transactions on pattern analysis and machine intelligence
Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide...

Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints.

IEEE journal of biomedical and health informatics
Semi-supervised learning is becoming an effective solution in medical image segmentation because annotations are costly and tedious to acquire. Methods based on the teacher-student model use consistency regularization and uncertainty estimation and h...