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Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research.

Scientific data
Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an avera...

Visualization of medical concepts represented using word embeddings: a scoping review.

BMC medical informatics and decision making
BACKGROUND: Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language p...

PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

Journal of biomedical informatics
The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our mo...

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

Journal of healthcare engineering
In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the ...

Diffeomorphic transforms for data augmentation of highly variable shape and texture objects.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts ca...

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram ...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Sensors (Basel, Switzerland)
Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-comple...

Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.

Journal of digital imaging
The external and middle ear conditions are diagnosed using a digital otoscope. The clinical diagnosis of ear conditions is suffered from restricted accuracy due to the increased dependency on otolaryngologist expertise, patient complaint, blurring of...

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues.

Journal of chemical information and modeling
In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of simila...

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion.

Sensors (Basel, Switzerland)
With the constantly growing popularity of video-based services and applications, no-reference video quality assessment (NR-VQA) has become a very hot research topic. Over the years, many different approaches have been introduced in the literature to ...