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 …
Deep learning applications in chest radiography and computed tomography: current state of the art
Deep learning is a genre of machine learning that allows computational models to learn
representations of data with multiple levels of abstraction using numerous processing layers …
representations of data with multiple levels of abstraction using numerous processing layers …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …
neural network, residual neural network, adversarial network; U-Net architectures are most …
A survey on artificial intelligence in pulmonary imaging
Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …
vision and image recognition creating widespread opportunities of using artificial …
AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients
This study aims to generate and also validate an automatic detection algorithm for
pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a …
pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a …
Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: A review
MS Diab, E Rodriguez-Villegas - IEEE Access, 2022 - ieeexplore.ieee.org
The use of machine learning in medical and assistive applications is receiving significant
attention thanks to the unique potential it offers to solve complex healthcare problems for …
attention thanks to the unique potential it offers to solve complex healthcare problems for …
Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi
T Nemoto, N Futakami, M Yagi… - Journal of radiation …, 2020 - academic.oup.com
This study aimed to examine the efficacy of semantic segmentation implemented by deep
learning and to confirm whether this method is more effective than a commercially dominant …
learning and to confirm whether this method is more effective than a commercially dominant …
Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural …
KA Hasenstab, N Yuan, T Retson… - Radiology …, 2021 - pubs.rsna.org
Purpose To develop a deep learning–based algorithm to stage the severity of chronic
obstructive pulmonary disease (COPD) through quantification of emphysema and air …
obstructive pulmonary disease (COPD) through quantification of emphysema and air …
[HTML][HTML] Clinical implementation of deep learning in thoracic radiology: potential applications and challenges
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic
radiology, are under active investigation with deep learning technology, which has shown …
radiology, are under active investigation with deep learning technology, which has shown …