87 research outputs found
A bone suppression model ensemble to improve COVID-19 detection in chest X-rays
Chest X-ray (CXR) is a widely performed radiology examination that helps to
detect abnormalities in the tissues and organs in the thoracic cavity.
Detecting pulmonary abnormalities like COVID-19 may become difficult due to
that they are obscured by the presence of bony structures like the ribs and the
clavicles, thereby resulting in screening/diagnostic misinterpretations.
Automated bone suppression methods would help suppress these bony structures
and increase soft tissue visibility. In this study, we propose to build an
ensemble of convolutional neural network models to suppress bones in frontal
CXRs, improve classification performance, and reduce interpretation errors
related to COVID-19 detection. The ensemble is constructed by (i) measuring the
multi-scale structural similarity index (MS-SSIM) score between the sub-blocks
of the bone-suppressed image predicted by each of the top-3 performing
bone-suppression models and the corresponding sub-blocks of its respective
ground truth soft-tissue image, and (ii) performing a majority voting of the
MS-SSIM score computed in each sub-block to identify the sub-block with the
maximum MS-SSIM score and use it in constructing the final bone-suppressed
image. We empirically determine the sub-block size that delivers superior bone
suppression performance. It is observed that the bone suppression model
ensemble outperformed the individual models in terms of MS-SSIM and other
metrics. A CXR modality-specific classification model is retrained and
evaluated on the non-bone-suppressed and bone-suppressed images to classify
them as showing normal lungs or other COVID-19-like manifestations. We observed
that the bone-suppressed model training significantly outperformed the model
trained on non-bone-suppressed images toward detecting COVID-19 manifestations.Comment: 29 pages, 10 figures, 4 table
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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