AIMC Topic: Deep Learning

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On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise.

Physics in medicine and biology
Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A de...

Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images.

The British journal of ophthalmology
BACKGROUND/AIMS: To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Chemical research in toxicology
Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an screening in the early stage...

DeepCLA: A Hybrid Deep Learning Approach for the Identification of Clathrin.

Journal of chemical information and modeling
Clathrin is a highly evolutionarily conserved protein, which can affect membrane cleavage and membrane release of vesicles. The absence of clathrin in the cellular system affects a variety of human diseases. Effective recognition of clathrin plays an...

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.

Journal of chemical information and modeling
Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, f...

Multidimensional Ground Reaction Forces and Moments From Wearable Sensor Accelerations via Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and...

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ...

Development and evaluation of deep learning for screening dental caries from oral photographs.

Oral diseases
OBJECTIVES: To develop and evaluate the performance of a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs.

Artificial intelligence in the early stages of drug discovery.

Archives of biochemistry and biophysics
Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.