AIMC Topic: Databases, Factual

Clear Filters Showing 311 to 320 of 3080 articles

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

IEEE transactions on neural networks and learning systems
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...

Medical Transformer: Universal Encoder for 3-D Brain MRI Analysis.

IEEE transactions on neural networks and learning systems
Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer...

Needle in a haystack: Harnessing AI in drug patent searches and prediction.

PloS one
The classification codes granted by patent offices are useful instruments for simplifying the bewildering variety of patents in existence. They are singularly unhelpful, however, in locating a specific subgroup of patents such as that of drug-related...

A multimodal approach for few-shot biomedical named entity recognition in low-resource languages.

Journal of biomedical informatics
In this study, we revisit named entity recognition (NER) in the biomedical domain from a multimodal perspective, with a particular focus on applications in low-resource languages. Existing research primarily relies on unimodal methods for NER, which ...

Biomedical document-level relation extraction with thematic capture and localized entity pooling.

Journal of biomedical informatics
In contrast to sentence-level relational extraction, document-level relation extraction poses greater challenges as a document typically contains multiple entities, and one entity may be associated with multiple other entities. Existing methods often...

Lightweight convolutional neural network for chest X-ray images classification.

Scientific reports
In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using pu...

A novel deep learning based method for myocardial strain quantification.

Biomedical physics & engineering express
This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination.We measure the global and regional myocardial stra...

A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets.

International journal of cardiology
BACKGROUND: Over the last few decades: heart disease (HD) has emerged as one of the deadliest diseases in the world. Approximately more than 31 % of the population dies from HD each year. The Diagnosis of HD in an earlier stage is a cognitively chall...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Computers in biology and medicine
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Journal of chemical information and modeling
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...