AIMC Topic: Deep Learning

Clear Filters Showing 12331 to 12340 of 28423 articles

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

Critical care medicine
OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill p...

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with...

Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning.

Biomedical engineering online
OBJECTIVES: This study aims to develop and evaluate the deep learning-based classification model for recognizing the pathology of renal tumor from macroscopic cross-section image.

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers.

Scientific reports
Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characteriza...

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.

Scientific reports
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We i...

Explainable deep learning for insights in El Niño and river flows.

Nature communications
The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-ra...

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

PLoS computational biology
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains ...

Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.

Journal of cancer research and clinical oncology
PURPOSE: We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment...

Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis.

Journal of digital imaging
Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the an...