AIMC Topic: Deep Learning

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Role of artificial intelligence in MS clinical practice.

NeuroImage. Clinical
Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and ma...

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.

Investigative radiology
OBJECTIVES: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can ...

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Medical image analysis
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic seg...

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...

Vision-based human fall detection systems using deep learning: A review.

Computers in biology and medicine
Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for ass...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Microbiology and immunology
Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes (MDGs) of host blood could be used to diagnose patients co-infected with HIV/TB....

Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks.

Sensors (Basel, Switzerland)
This paper investigates the computation offloading problem in mobile edge computing (MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the MEC network by jointly optimizing the offloading decision and bandwidth alloc...

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.

Sensors (Basel, Switzerland)
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the s...

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Scientific reports
Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coo...