AIMC Topic: Deep Learning

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Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Annals of the New York Academy of Sciences
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....

Ultrasound-based deep learning to differentiate salivary gland tumors.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: Accurate preoperative diagnosis is essential for selecting appropriate surgical interventions. This study aims to develop a deep learning model based on ultrasound (US) imaging to accurately differentiate between benign and malignant saliv...

MuSIA: Exploiting multi-source information fusion with abnormal activations for out-of-distribution detection.

Neural networks : the official journal of the International Neural Network Society
In the open world, out-of-distribution (OOD) detection is crucial to ensure the reliability and robustness of deep learning models. Traditional OOD detection methods are often limited to using single-source information coupled with the abnormal activ...

Multimodal Deep Learning for Grading Carpal Tunnel Syndrome: A Multicenter Study in China.

Academic radiology
RATIONALE AND OBJECTIVES: Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimo...

Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.

Sleep medicine
Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manual analysis to differentiate obstructive sleep apnea (OSA) from central sleep apnea (CSA). Existing portable devices, while valuable in detecting slee...

A review of denoising methods in single-particle cryo-EM.

Micron (Oxford, England : 1993)
Cryo-EM has become a vital technique for resolving macromolecular structures at near-atomic resolution, enabling the visualization of proteins and large molecular complexes. However, the images are often accompanied by extremely low SNR, which poses ...

Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

Ultrasonic imaging
Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was c...

Proton dose calculation with transformer: Transforming spot map to dose.

Medical physics
BACKGROUND: Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade-off between computational efficiency and accu...

Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.

Medical image analysis
We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique interactive and spatially heterogeneous nature of this application to develop a registration algori...

Deep structural brain imaging via computational three-photon microscopy.

Journal of biomedical optics
SIGNIFICANCE: High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.