AIMC Topic: Deep Learning

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Automatic 3D pelvimetry framework in CT images and its validation.

Scientific reports
In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial structure and morphology of pelvis, which can be represented by pelvic parameters. Pelvic parameters, including pelvic incidence, pelvic tilt and sacral ...

An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI.

Biomedical physics & engineering express
Some pathologies such as cancer and dementia require multiple imaging modalities to fully diagnose and assess the extent of the disease. Magnetic resonance imaging offers this kind of polyvalence, but examinations take time and can require contrast a...

Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Phytopathology
Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection...

Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Journal of imaging informatics in medicine
PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.

In-vitro blood purification using tiny pinch holographic optical tweezers based on deep learning.

Biosensors & bioelectronics
In-vitro blood purification is essential to a wide range of medical treatments, requiring fine-grained analysis and precise separation of blood components. Despite existing methods that can extract specific components from blood by size or by magneti...

Lite-YOLOv8: a more lightweight algorithm for Tubercle Bacilli detection.

Medical & biological engineering & computing
Deep learning is a transformative force in the medical field and it has made significant progress as a pivotal alternative to conventional manual testing methods. Detection of Tubercle Bacilli in sputum samples is faced with the problems of complex b...

Skeleton-guided 3D convolutional neural network for tubular structure segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network...

A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehe...

Gluconeogenesis unraveled: A proteomic Odyssey with machine learning.

Methods (San Diego, Calif.)
The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately t...

A deep learning phase-based solution in 2D echocardiography motion estimation.

Physical and engineering sciences in medicine
In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from ...