AIMC Topic: Deep Learning

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Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

International journal of molecular sciences
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysi...

MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain.

Scientific reports
Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the di...

Embodied intelligence via learning and evolution.

Nature communications
The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. Howev...

KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-Clinical Keratoconus Detection Based on Raw Data of the Pentacam HR System.

IEEE journal of biomedical and health informatics
Keratoconus is one of the most severe corneal diseases, which is difficult to detect at the early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called ...

Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.

Eye (London, England)
BACKGROUND/OBJECTIVES: This study aimed to evaluate a deep learning model for estimating uncorrected refractive error using posterior segment optical coherence tomography (OCT) images.

Symbolic Deep Networks: A Psychologically Inspired Lightweight and Efficient Approach to Deep Learning.

Topics in cognitive science
The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept i...

Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system.

Oral radiology
OBJECTIVES: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography.

Recovering SWI-filtered phase data using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs.

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.

Radiology
Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI...

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

Physical and engineering sciences in medicine
According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical f...