AIMC Topic: Reproducibility of Results

Clear Filters Showing 2411 to 2420 of 5908 articles

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

The American journal of pathology
Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and limitation in terms of interpretability are significant obstacles for their widespread clinical uti...

Deep learning models for benign and malign ocular tumor growth estimation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm f...

Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.

The Lancet. Digital health
BACKGROUND: Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable,...

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.

Radiology
Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of...

Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction.

Nature communications
A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease resear...

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics.

Yearbook of medical informatics
OBJECTIVE: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.

DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.

eLife
Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few ...

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

The Lancet. Digital health
BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening te...

Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under ...