AIMC Topic: Deep Learning

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Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more tr...

High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.

Biomedical physics & engineering express
Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-...

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.

European radiology
OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM).

Using Deep Learning to Automate Goldmann Applanation Tonometry Readings.

Ophthalmology
PURPOSE: To develop an objective and automated method for measuring intraocular pressure using deep learning and fixed-force Goldmann applanation tonometry (GAT) techniques.

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.

Neuroradiology
PURPOSE: Natural language processing (NLP) can be used for automatic flagging of radiology reports. We assessed deep learning models for classifying non-English head CT reports.

A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.

Lung cancer (Amsterdam, Netherlands)
OBJECTIVES: The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lung cancer patients; erroneous evaluations result in inappropriate therapeutic plans and increase the ...

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning.

Biomolecules
A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acqui...

DART: Domain-Adversarial Residual-Transfer networks for unsupervised cross-domain image classification.

Neural networks : the official journal of the International Neural Network Society
The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but ga...

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction.

Journal of chemical information and modeling
Advances in deep neural network (DNN)-based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolutional neural networks (GCNNs) reporting state-of-the-art pe...

Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation.

Magnetic resonance in medicine
PURPOSE: In this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT-CMR) data powered by deep learning.