AIMC Topic: Deep Learning

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Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

Journal of neuro-oncology
PURPOSE: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, d...

Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals.

IEEE journal of biomedical and health informatics
Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interic...

Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting.

IEEE journal of biomedical and health informatics
Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models hav...

End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning.

IEEE journal of biomedical and health informatics
OBJECTIVE: Mean intracranial pressure (ICP) is commonly used in the management of patients with intracranial pathologies. However, the shape of the ICP signal over a single cardiac cycle, called ICP pulse waveform, also contains information on the st...

Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.

BMJ open ophthalmology
OBJECTIVE: To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).

Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection.

PloS one
With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, ...

Monitoring ecosystem services in the Guangdong-Hong Kong-Macao Greater Bay Area based on multi-temporal deep learning.

The Science of the total environment
Assessment of ecosystem service supply and demand, as well as the budgets of ecosystem service supply and demand, is the basis of scientific urban planning. In the 20 years between the proposal and formation of the Guangdong-Hong Kong-Macao Greater B...

A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

Magnetic resonance imaging
PURPOSE: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification.

FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI.

NeuroImage
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) s...

Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification.

IEEE transactions on pattern analysis and machine intelligence
Part-level representations are important for robust person re-identification (ReID), but in practice feature quality suffers due to the body part misalignment problem. In this paper, we present a robust, compact, and easy-to-use method called the Mul...