External validation of deep learning algorithms for radiologic diagnosis: a systematic review
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
Vision Transformers in medical computer vision—A contemplative retrospection
Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …
contained within images, have evolved as one of the most contemporary and dominant …
Gut microbiome, big data and machine learning to promote precision medicine for cancer
The gut microbiome has been implicated in cancer in several ways, as specific microbial
signatures are known to promote cancer development and influence safety, tolerability and …
signatures are known to promote cancer development and influence safety, tolerability and …
An integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals
Deep learning, for image data processing, has been widely used to solve a variety of
problems related to medical practices. However, researchers are constantly struggling to …
problems related to medical practices. However, researchers are constantly struggling to …
Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
[HTML][HTML] Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images
Objective To investigate the impact of harmonization on the performance of CT, PET, and
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …
Dynamic points agglomeration for hierarchical point sets learning
Many previous works on point sets learning achieve excellent performance with hierarchical
architecture. Their strategies towards points agglomeration, however, only perform points …
architecture. Their strategies towards points agglomeration, however, only perform points …
Artificial intelligence applications for thoracic imaging
G Chassagnon, M Vakalopoulou, N Paragios… - European journal of …, 2020 - Elsevier
Artificial intelligence is a hot topic in medical imaging. The development of deep learning
methods and in particular the use of convolutional neural networks (CNNs), have led to …
methods and in particular the use of convolutional neural networks (CNNs), have led to …