AIMC Topic: Deep Learning

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Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Medicine
Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires c...

Development of a pressure ulcer stage determination system for community healthcare providers using a vision transformer deep learning model.

Medicine
This study reports the first steps toward establishing a computer vision system to help caregivers of bedridden patients detect pressure ulcers (PUs) early. While many previous studies have focused on using convolutional neural networks (CNNs) to ele...

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep
STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

Nucleic acids research
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors li...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Cerebral cortex (New York, N.Y. : 1991)
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated t...

Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.

American journal of epidemiology
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou a...

ParaSurf: a surface-based deep learning approach for paratope-antigen interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving ...

Automated Detection of Retinal Detachment Using Deep Learning-Based Segmentation on Ocular Ultrasonography Images.

Translational vision science & technology
PURPOSE: This study aims to develop an automated pipeline to detect retinal detachment from B-scan ocular ultrasonography (USG) images by using deep learning-based segmentation.

Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

Translational vision science & technology
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...