AIMC Topic: Deep Learning

Clear Filters Showing 20401 to 20410 of 28423 articles

Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy.

Medical physics
PURPOSE: In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP...

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

Nature cancer
We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approac...

Pan-cancer image-based detection of clinically actionable genetic alterations.

Nature cancer
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learnin...

Revealing architectural order with quantitative label-free imaging and deep learning.

eLife
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to ...

Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning.

PloS one
Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized...

Machine learning for modeling animal movement.

PloS one
Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior...

Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation.

Hellenic journal of nuclear medicine
OBJECTIVE: To investigate a deep learning technique, more specifically state-of-the-art convolutional neural networks (CNN), for automatic characterization of polar maps derived from myocardial perfusion imaging (MPI) studies for the diagnosis of cor...

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection ...

A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data.

Proteomics
Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of v...

Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation.

Molecular ecology resources
For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong ...