Abstract：
:The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to alleviate the limitations of small datasets. We have previously developed an image blending tool that allows users to modify or supplement an existing CT or mammography dataset by seamlessly inserting a lesion extracted from a source image into a target image. This tool also provides the option to apply various types of transformations to different properties of the lesion prior to its insertion into a new location. In this study, we used this tool to create synthetic samples that appear realistic in chest CT. We then augmented different size training sets with these artificial samples, and investigated the effect of the augmentation on training various classifiers for the detection of lung nodules. Our results indicate that the proposed lesion insertion method can improve classifier performance for small training datasets, and thereby help reduce the need to acquire and label actual patient data.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Pezeshk A,Petrick N,Weijie Chen,Sahiner Bdoi
10.1109/TMI.2016.2640180subject
Has Abstractpub_date
2017-04-01 00:00:00pages
1005-1015issue
4eissn
0278-0062issn
1558-254Xjournal_volume
36pub_type
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