AIMC Topic: Deep Learning

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DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantif...

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.

Journal of chemical information and modeling
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a s...

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...

Prostate Cancer Detection using Deep Convolutional Neural Networks.

Scientific reports
Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intens...

The METLIN small molecule dataset for machine learning-based retention time prediction.

Nature communications
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retent...

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.

BMC medical genomics
BACKGROUND: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also importan...

Submillimeter MR fingerprinting using deep learning-based tissue quantification.

Magnetic resonance in medicine
PURPOSE: To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach.

Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea.

IEEE journal of biomedical and health informatics
The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overc...