AIMC Topic: Deep Learning

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Deep Learning in Knee MRI: A Prospective Study to Enhance Efficiency, Diagnostic Confidence and Sustainability.

Academic radiology
RATIONALE AND OBJECTIVES: The objective of this study was to evaluate a combination of deep learning (DL)-reconstructed parallel acquisition technique (PAT) and simultaneous multislice (SMS) acceleration imaging in comparison to conventional knee ima...

BenchXAI: Comprehensive benchmarking of post-hoc explainable AI methods on multi-modal biomedical data.

Computers in biology and medicine
The increasing digitalization of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical ...

Deep reinforcement learning for Type 1 Diabetes: Dual PPO controller for personalized insulin management.

Computers in biology and medicine
BACKGROUND: Managing blood glucose levels in Type 1 Diabetes Mellitus (T1DM) is essential to prevent complications. Traditional insulin delivery methods often require significant patient involvement, limiting automation. Reinforcement Learning (RL)-b...

Combining Deep Data-Driven and Physics-Inspired Learning for Shear Wave Speed Estimation in Ultrasound Elastography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The shear wave elastography (SWE) provides quantitative markers for tissue characterization by measuring the shear wave speed (SWS), which reflects tissue stiffness. SWE uses an acoustic radiation force pulse sequence to generate shear waves that pro...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Computers in biology and medicine
Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascular conditions. Detecting these anomalies early through electrocardiogram (ECG) signal analysis is critical for preventive healthcare and effective tre...

Multi-level feature fusion network for kidney disease detection.

Computers in biology and medicine
Kidney irregularities pose a significant public health challenge, often leading to severe complications, yet the limited availability of nephrologists makes early detection costly and time-consuming. To address this issue, we propose a deep learning ...

Lag-Net: Lag correction for cone-beam CT via a convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Due to the presence of charge traps in amorphous silicon flat-panel detectors, lag signals are generated in consecutively captured projections. These signals lead to ghosting in projection images and severe lag artifacts in ...

A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Sparse arrays offer several advantages over other element reduction techniques for 3-D ultrasound imaging. However, the large interelement spacing in these arrays results in high sidelobe-related artifacts, which significantly degrade image quality a...

ChatIOS: Improving automatic 3-dimensional tooth segmentation via GPT-4V and multimodal pre-training.

Journal of dentistry
OBJECTIVES: This study aims to propose a framework that integrates GPT-4V, a recent advanced version of ChatGPT, and multimodal pre-training techniques to enhance deep learning algorithms for 3-dimensional (3D) tooth segmentation in scans produced by...