AIMC Topic: Deep Learning

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DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes.

Computational biology and chemistry
B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including des...

A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers.

The British journal of radiology
OBJECTIVES: To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning-based algorithm (CS-AI) using convolutional neural networks to accelerate 2D MRI of the knee.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Sensors (Basel, Switzerland)
Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and ...

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

Medical physics
BACKGROUND: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnos...

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

International journal of computer assisted radiology and surgery
PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. T...

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution.

Computers in biology and medicine
CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aid...

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

The Lancet. Digital health
BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available expert...

Deep learning for optimization of protein expression.

Current opinion in biotechnology
Advances in high-throughput DNA synthesis and sequencing have fuelled the use of massively parallel reporter assays for strain characterization. These experiments produce large datasets that map DNA sequences to protein expression levels, and have sp...

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.

The Lancet. Digital health
BACKGROUND: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer ...

Left ventricle segmentation combining deep learning and deformable models with anatomical constraints.

Journal of biomedical informatics
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed, in...