AIMC Topic: Deep Learning

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New trend in artificial intelligence-based assistive technology for thoracic imaging.

La Radiologia medica
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggere...

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction.

Journal of chemical information and modeling
Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification...

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

International journal of molecular sciences
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designe...

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.

Molecular diversity
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this p...

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole ...

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor...

Self-supervised deep learning for joint 3D low-dose PET/CT image denoising.

Computers in biology and medicine
Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET) and low-dose computed tomography (LDCT) has been widely explored. However, previous methods have focused only on single modality denoising, neglecting the possibility...

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T.

NMR in biomedicine
At ultrahigh field strengths images of the body are hampered by B -field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a "bias field" to the ideal image. Current bias field correction m...