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A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
ABSTRACT The Coronavirus (COVID-19) outbreak in December 2019 has drastically
affected humans worldwide, creating a health crisis that has infected millions of lives and …
affected humans worldwide, creating a health crisis that has infected millions of lives and …
Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai
Abstract Large Vision-Language Models (LVLMs) are capable of handling diverse data
types such as imaging, text, and physiological signals, and can be applied in various fields …
types such as imaging, text, and physiological signals, and can be applied in various fields …
COLI‐Net: deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography …
We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography …
Novel autosegmentation spatial similarity metrics capture the time required to correct segmentations better than traditional metrics in a thoracic cavity segmentation …
Automated segmentation templates can save clinicians time compared to de novo
segmentation but may still take substantial time to review and correct. It has not been …
segmentation but may still take substantial time to review and correct. It has not been …
Learning label diffusion maps for semi-automatic segmentation of lung CT images with COVID-19
Deep Learning (DL) has become one of the key approaches for dealing with many
challenges in medical imaging, which includes lung segmentation in Computed …
challenges in medical imaging, which includes lung segmentation in Computed …
Semi-supervised covid-19 volumetric pulmonary lesion estimation on ct images using probabilistic active contour and cnn segmentation
Purpose A semi-supervised two-step methodology is proposed to obtain a volumetric
estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods …
estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods …
Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different …
Purpose This study aims at exploiting artificial intelligence (AI) for the identification,
segmentation and quantification of COVID-19 pulmonary lesions. The limited data …
segmentation and quantification of COVID-19 pulmonary lesions. The limited data …
[HTML][HTML] Automated detection, segmentation, and classification of pleural effusion from computed tomography scans using machine learning
R Sexauer, S Yang, T Weikert, J Poletti… - Investigative …, 2022 - journals.lww.com
Objective This study trained and evaluated algorithms to detect, segment, and classify
simple and complex pleural effusions on computed tomography (CT) scans. Materials and …
simple and complex pleural effusions on computed tomography (CT) scans. Materials and …
Local style transfer via latent space manipulation for cross-disease lesion segmentation
Automaticlesion segmentation is important for assisting doctors in the diagnostic process.
Recent deep learning approaches heavily rely on large-scale datasets, which are difficult to …
Recent deep learning approaches heavily rely on large-scale datasets, which are difficult to …
Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis
Purpose To assess the impact of lung segmentation accuracy in an automatic pipeline for
quantitative analysis of CT images. Methods Four different platforms for automatic lung …
quantitative analysis of CT images. Methods Four different platforms for automatic lung …