Building medical image classifiers with very limited data using segmentation networks
Sign Up to like & getrecommendations! Published in 2018 at "Medical Image Analysis"
DOI: 10.1016/j.media.2018.07.010
Abstract: HighlightsBuilding medical image classifiers using features from segmentation networks.Important for 3D image analysis as ImageNet pre‐trained CNNs are unavailable in 3D.Tested on 3D brain tumor type and 2D cardiac semantic level classifications.Compared to classifiers trained… read more here.
Keywords: image; classification; segmentation networks; image classifiers ... See more keywords
Learning and Adaptation From Minimum Samples With Heterogeneous Quality: An Investigation of Image Segmentation Networks on Natural Dataset
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3275748
Abstract: Training deep learning-based image segmentation networks require large number of samples of adequate quality. However, obtaining large number of samples is not possible in certain domains. Recent approaches use augmentation and transfer learning techniques to… read more here.
Keywords: quality; image segmentation; segmentation networks; heterogeneous quality ... See more keywords
Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of X-ray science and technology"
DOI: 10.3233/xst-211113
Abstract: BACKGROUND Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster that PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models. OBJECTIVE This… read more here.
Keywords: covid; detection; segmentation networks; cxr images ... See more keywords
Semantic Segmentation of Medical Images Based on Runge–Kutta Methods
Sign Up to like & getrecommendations! Published in 2023 at "Bioengineering"
DOI: 10.3390/bioengineering10050506
Abstract: In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder–decoder structure. However, the design of the segmentation networks is fragmented… read more here.
Keywords: segmentation; runge kutta; segmentation networks; semantic segmentation ... See more keywords
Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images
Sign Up to like & getrecommendations! Published in 2022 at "Diagnostics"
DOI: 10.3390/diagnostics12081788
Abstract: Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely… read more here.
Keywords: kidney stones; deep segmentation; unenhanced abdominal; kidney ... See more keywords