Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
Quantum machine learning architecture for COVID-19 classification based on synthetic data generation using conditional adversarial neural network
Background COVID-19 is a novel virus that affects the upper respiratory tract, as well as the
lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are …
lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are …
A bi-stage feature selection approach for COVID-19 prediction using chest CT images
The rapid spread of coronavirus disease has become an example of the worst disruptive
disasters of the century around the globe. To fight against the spread of this virus, clinical …
disasters of the century around the globe. To fight against the spread of this virus, clinical …
Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan
This research work aims to identify COVID-19 through deep learning models using lung CT-
SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural …
SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural …
A k-mer Based Approach for SARS-CoV-2 Variant Identification
With the rapid spread of the novel coronavirus (COVID-19) across the globe and its
continuous mutation, it is of pivotal importance to design a system to identify different known …
continuous mutation, it is of pivotal importance to design a system to identify different known …
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound
Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …
representations for transfer learning, leveraging large amounts of unlabelled data. This …
Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging
D Wolf, T Payer, CS Lisson, CG Lisson, M Beer… - Scientific Reports, 2023 - nature.com
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors,
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models …
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models …
Binary classification of covid-19 ct images using cnn: Covid diagnosis using ct
COVID-19 pandemic has hit the world with such a force that the world's leading economies
are finding it challenging to come out of it. Countries with the best medical facilities are even …
are finding it challenging to come out of it. Countries with the best medical facilities are even …
Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform
The COVID-19 epidemic has been causing a global problem since December 2019. COVID-
19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is …
19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is …
Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network
J Amin, MA Anjum, M Sharif, A Rehman… - Microscopy research …, 2022 - Wiley Online Library
The detection of biological RNA from sputum has a comparatively poor positive rate in the
initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a …
initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a …