AIMC Topic: Deep Learning

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Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

NeuroImage
Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are inter...

Beyond Woodward-Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy.

Journal of chemical information and modeling
An adequate understanding of molecular structure-property relationships is important for developing new molecules with desired properties. Although deep learning optical spectroscopy (DLOS) has been successfully applied to predict the optical and pho...

Deep learning identification of stiffness markers in breast cancer.

Biomaterials
While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in...

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four...

Deep-Learning Based Adaptive Ultrasound Imaging From Sub-Nyquist Channel Data.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challe...

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

Sensors (Basel, Switzerland)
The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways...

LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction.

Scientific reports
Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a n...

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

Physics in medicine and biology
In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estima...

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning.

Computational and mathematical methods in medicine
It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restorati...

Index Evaluation of Different Hospital Management Modes Based on Deep Learning Model.

Computational intelligence and neuroscience
In order to effectively improve the efficiency of hospital public management, we designed a hospital management index system based on deep learning model and analysed the application effect of reverse broadcast neural network model in hospital. The r...