AIMC Topic: Tomography, X-Ray Computed

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An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

Journal of medical primatology
BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis,...

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

The British journal of radiology
OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD.

Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network.

Postgraduate medical journal
PURPOSE: To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance.

Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models.

American journal of respiratory and critical care medicine
The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population because of limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial i...

Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsu...

The pseudo-siamese framework combines Transformer and CNN for medical image generation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multi-phase medical imaging can provide significant improvement in disease multi-modal diagnosis. However, medical image data often suffer from modality missing issues. Therefore, synthesizing missing phases using available phases is of great clinica...

Feature Imitating Networks Enhance the Performance, Reliability and Speed of Deep Learning on Biomedical Image Processing Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks to enhance their performance. In this work, we perform the first evalua...

Toward Autonomous Marker Localization for Lumbar Epidural Steroid Injection Robot.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study compares the effectiveness of the traditional minimum circle detection strategy, i.e. Welzl's algorithm, and the state-of-the-art nnU-Net in the localization of lumbar Epidural Steroid Injection (ESI) robot markers across different imaging...

Fuzzy-Label Weighted Deep Learning Classification for CT Image Quality Evaluation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper proposes a fuzzy-label weighted deep learning-based image classification approach for assessing computed tomography (CT) image quality. More specifically, we want to determine if a captured CT image passes Quality Assessment (QA) with a ce...