AIMC Topic: Deep Learning

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Applying deep learning to quantify empty lacunae in histologic sections of osteonecrosis of the femoral head.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Osteonecrosis of the femoral head (ONFH) is a disease in which inadequate blood supply to the subchondral bone causes the death of cells in the bone marrow. Decalcified histology and assessment of the percentage of empty lacunae are used to quantify ...

Deep learning in target prediction and drug repositioning: Recent advances and challenges.

Drug discovery today
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug ...

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Magnetic resonance imaging
Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal...

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

SLAS discovery : advancing life sciences R & D
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-con...

AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algori...

Explainable deep learning ensemble for food image analysis on edge devices.

Computers in biology and medicine
Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges su...

A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Cell reports methods
MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate...

New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

IEEE transactions on neural networks and learning systems
The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies...

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography.

IEEE transactions on medical imaging
Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are ...

Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation.

IEEE transactions on medical imaging
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervise...