Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
[HTML][HTML] Pathology image analysis using segmentation deep learning algorithms
With the rapid development of image scanning techniques and visualization software, whole
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
G Chassagnon, M Vakalopoulou, E Battistella… - Medical image …, 2021 - Elsevier
Abstract Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around
the world rapidly. Computed tomography (CT) imaging has been proven to be an important …
the world rapidly. Computed tomography (CT) imaging has been proven to be an important …
Multi-atlas segmentation of biomedical images: a survey
JE Iglesias, MR Sabuncu - Medical image analysis, 2015 - Elsevier
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
Training strategies for radiology deep learning models in data-limited scenarios
Data-driven approaches have great potential to shape future practices in radiology. The
most straightforward strategy to obtain clinically accurate models is to use large, well …
most straightforward strategy to obtain clinically accurate models is to use large, well …
State-of-the-art retinal vessel segmentation with minimalistic models
The segmentation of retinal vasculature from eye fundus images is a fundamental task in
retinal image analysis. Over recent years, increasingly complex approaches based on …
retinal image analysis. Over recent years, increasingly complex approaches based on …
Disentangled representation learning in cardiac image analysis
Typically, a medical image offers spatial information on the anatomy (and pathology)
modulated by imaging specific characteristics. Many imaging modalities including Magnetic …
modulated by imaging specific characteristics. Many imaging modalities including Magnetic …
AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays
Artificial intelligence (AI) has played a significant role in image analysis and feature
extraction, applied to detect and diagnose a wide range of chest-related diseases. Although …
extraction, applied to detect and diagnose a wide range of chest-related diseases. Although …
Anatomy-aided deep learning for medical image segmentation: a review
Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …
years. However, despite these advances, there are still problems for which DL-based …