AIMC Topic: Deep Learning

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Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images.

Medical image analysis
The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurate...

Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of t...

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet.

Proceedings of the National Academy of Sciences of the United States of America
Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimens...

Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data.

BMC medical genomics
BACKGROUND: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and h...

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.

BMC bioinformatics
BACKGROUND: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying A...

Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches.

BMC medical informatics and decision making
BACKGROUND: Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigat...

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

NMR in biomedicine
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. Howev...

Automatic cancer tissue detection using multispectral photoacoustic imaging.

International journal of computer assisted radiology and surgery
PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tiss...

Prediction of lower-grade glioma molecular subtypes using deep learning.

Journal of neuro-oncology
INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.

Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures.

Molecular metabolism
OBJECTIVE: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that enc...