Deep learning and medical image analysis for COVID-19 diagnosis and prediction
The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
[HTML][HTML] TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …
different challenges compared to RGB images typically used in computer vision. These …
Role of machine learning in medical research: A survey
Abstract Machine learning is one of the essential and effective tools in analyzing highly
complex medical data. With vast amounts of medical data being generated, there is an …
complex medical data. With vast amounts of medical data being generated, there is an …
Deep learning approaches for detecting COVID-19 from chest X-ray images: A survey
Chest X-ray (CXR) imaging is a standard and crucial examination method used for
suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited …
suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited …
Fine-tuning U-Net for ultrasound image segmentation: different layers, different outcomes
One way of resolving the problem of scarce and expensive data in deep learning for medical
applications is using transfer learning and fine-tuning a network which has been trained on …
applications is using transfer learning and fine-tuning a network which has been trained on …
Automatic detection of rare pathologies in fundus photographs using few-shot learning
In the last decades, large datasets of fundus photographs have been collected in diabetic
retinopathy (DR) screening networks. Through deep learning, these datasets were used to …
retinopathy (DR) screening networks. Through deep learning, these datasets were used to …
[HTML][HTML] Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies
W Hryniewska, P Bombiński, P Szatkowski… - Pattern Recognition, 2021 - Elsevier
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most
important global problems today. In a short period of time, it has led to the development of …
important global problems today. In a short period of time, it has led to the development of …
Multi-learner based deep meta-learning for few-shot medical image classification
Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost
of establishing high-quality medical datasets. Many FSL approaches have been proposed in …
of establishing high-quality medical datasets. Many FSL approaches have been proposed in …
Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks
The current COVID-19 pandemic has motivated the researchers to use artificial intelligence
techniques for a potential alternative to reverse transcription-polymerase chain reaction due …
techniques for a potential alternative to reverse transcription-polymerase chain reaction due …