966 research outputs found
Transfer learning for multicenter classification of chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is a lung disease which can be
quantified using chest computed tomography (CT) scans. Recent studies have
shown that COPD can be automatically diagnosed using weakly supervised learning
of intensity and texture distributions. However, up till now such classifiers
have only been evaluated on scans from a single domain, and it is unclear
whether they would generalize across domains, such as different scanners or
scanning protocols. To address this problem, we investigate classification of
COPD in a multi-center dataset with a total of 803 scans from three different
centers, four different scanners, with heterogenous subject distributions. Our
method is based on Gaussian texture features, and a weighted logistic
classifier, which increases the weights of samples similar to the test data. We
show that Gaussian texture features outperform intensity features previously
used in multi-center classification tasks. We also show that a weighting
strategy based on a classifier that is trained to discriminate between scans
from different domains, can further improve the results. To encourage further
research into transfer learning methods for classification of COPD, upon
acceptance of the paper we will release two feature datasets used in this study
on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
Content based retrieval of PET neurological images
Medical image management has posed challenges to many researchers, especially when the images have to be indexed and retrieved using their visual content that is meaningful to clinicians. In this study, an image retrieval system has been developed for 3D brain PET (Position emission tomography) images. It has been found that PET neurological images can be retrieved based upon their diagnostic status using only data pertaining to their content, and predominantly the visual content.
During the study PET scans are spatially normalized, using existing techniques, and their visual data is quantified. The mid-sagittal-plane of each individual 3D PET scan is found and then utilized in the detection of abnormal asymmetries, such as tumours or physical injuries. All the asymmetries detected are referenced to the Talairarch and Tournoux anatomical atlas. The Cartesian co- ordinates in Talairarch space, of detected lesion, are employed along with the associated anatomical structure(s) as the indices within the content based image retrieval system. The anatomical atlas is then also utilized to isolate distinct anatomical areas that are related to a number of neurodegenerative disorders. After segmentation of the anatomical regions of interest algorithms are applied to characterize the texture of brain intensity using Gabor filters and to elucidate the mean index ratio of activation levels. These measurements are combined to produce a single feature vector that is incorporated into the content based image retrieval system.
Experimental results on images with known diagnoses show that physical lesions such as head injuries and tumours can be, to a certain extent, detected correctly. Images with correctly detected and measured lesion are then retrieved from the database of images when a query pertains to the measured locale. Images with neurodegenerative disorder patterns have been indexed and retrieved via texture-based features. Retrieval accuracy is increased, for images from patients diagnosed with dementia, by combining the texture feature and mean index ratio value
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