Accurate Determination of Imaging Modality using an Ensemble of Text- and Image-Based Classifiers |
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Authors: | Charles E Kahn Jr Jayashree Kalpathy-Cramer Cesar A Lam Christina E Eldredge |
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Institution: | (1) Department of Radiology, Medical College of Wisconsin, 9200 W. Wisconsin Ave., Milwaukee, WI 53226, USA;(2) Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Sciences University, Portland, OR, USA;(3) Department of Family and Community Medicine, Medical College of Wisconsin, Milwaukee, WI, USA |
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Abstract: | Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether
an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from
radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned
one of eight imaging modalities—computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission
tomography, photograph, ultrasound, or radiograph—to each image based on visual and/or textual information. Three physicians
determined the modality of 5,000 randomly selected images as a reference standard. A “Simple Vote” ensemble classifier assigned
each image to the modality that received the greatest number of individual classifiers’ votes. A “Weighted Vote” classifier
weighted each individual classifier’s vote based on performance over a training set. For each image, this classifier’s output
was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic
mean of precision and recall) for each classifier. Individual classifiers’ F scores ranged from 0.184 to 0.892. The simple
vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905–0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927–0.941), respectively. The weighted vote classifier performed significantly better
than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our
sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve
systems for image retrieval. |
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Keywords: | Computer vision Content-based image retrieval Digital libraries Image analysis Image retrieval Classification Data mining |
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