AIMC Topic: Deep Learning

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Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.

Medical physics
BACKGROUND: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation c...

Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

ACS chemical biology
Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed ...

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach.

Sensors (Basel, Switzerland)
A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural g...

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing.

Sensors (Basel, Switzerland)
High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-tim...

Tracking cell lineages in 3D by incremental deep learning.

eLife
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading curre...

Accelerated cardiac T mapping in four heartbeats with inline MyoMapNet: a deep learning-based T estimation approach.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PURPOSE: To develop and evaluate MyoMapNet, a rapid myocardial T mapping approach that uses fully connected neural networks (FCNN) to estimate T values from four T-weighted images collected after a single inversion pulse in four heartbeats (Look-Lock...

Attention based automated radiology report generation using CNN and LSTM.

PloS one
The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches ...

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

American journal of ophthalmology
PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images.

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

Ophthalmology. Retina
PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derive...

Pyrolytic characteristics of fine materials from municipal solid waste using TG-FTIR, Py-GC/MS, and deep learning approach: Kinetics, thermodynamics, and gaseous products distribution.

Chemosphere
Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning ...