AIMC Topic: Deep Learning

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A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions-A Deep Learning Approach.

IEEE journal of biomedical and health informatics
This study examined the effectiveness of a vision-based framework for multiple sclerosis (MS) and Parkinson's disease (PD) gait dysfunction prediction. We collected gait video data from multi-view digital cameras during self-paced walking from MS, PD...

Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases.

IEEE journal of biomedical and health informatics
Fluorescence imaging-based diagnostic systems have been widely used to diagnose skin diseases due to their ability to provide detailed information related to the molecular composition of the skin compared to conventional RGB imaging. In addition, rec...

Deep Neural Forest for Out-of-Distribution Detection of Skin Lesion Images.

IEEE journal of biomedical and health informatics
Deep learning methods have shown outstanding potential in dermatology for skin lesion detection and identification. However, they usually require annotations beforehand and can only classify lesion classes seen in the training set. Moreover, large-sc...

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Journal of digital imaging
Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic ...

Improving SWATH-MS analysis by deep-learning.

Proteomics
Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectra...

Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study.

Journal of the American Society for Mass Spectrometry
In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their dist...

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

Proceedings of the National Academy of Sciences of the United States of America
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates...

Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs.

JAMA network open
IMPORTANCE: Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

JAMA network open
IMPORTANCE: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiolo...

Deep Learning Framework for Controlling Work Sequence in Collaborative Human-Robot Assembly Processes.

Sensors (Basel, Switzerland)
The human-robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human's state or on specific gestures purposely performed by the human, thus increasing the time requi...