Training of polyp staging systems using mixed imaging modalities. (1st November 2018)

• Record Type: Journal Article
• Authors: Wimmer, Georg; Gadermayr, Michael; Kwitt, Roland; Häfner, Michael; Tamaki, Toru; Yoshida, Shigeto; Tanaka, Shinji; Merhof, Dorit; Uhl, Andreas
• Is Part Of: Computers in biology and medicine. Volume 102(2018)
• Page Start: 251
• Page End: 259
• Publication Date: 2018-11-01
• Subjects: Polyp staging -- Narrow-band imaging -- Chromoscopy -- Endoscopy -- I-scan -- Computer-assisted diagnosis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.05.003
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: In medical image data sets, the number of images is usually quite small. The small number of training samples does not allow to properly train classifiers which leads to massive overfitting to the training data. In this work, we investigate whether increasing the number of training samples by merging datasets from different imaging modalities can be effectively applied to improve predictive performance. Further, we investigate if the extracted features from the employed image representations differ between different imaging modalities and if domain adaption helps to overcome these differences. Method: We employ twelve feature extraction methods to differentiate between non-neoplastic and neoplastic lesions. Experiments are performed using four different classifier training strategies, each with a different combination of training data. The specifically designed setup for these experiments enables a fair comparison between the four training strategies. Results: Combining high definition with high magnification training data and chromoscopic with non-chromoscopic training data partly improved the results. The usage of domain adaptation has only a small effect on the results compared to just using non-adapted training data. Conclusion: Merging datasets from different imaging modalities turned out to be partially beneficial for the case of combining high definition endoscopic data with high magnification endoscopic data and for combining chromoscopic with non-chromoscopic data. NBI and chromoendoscopy on the other hand are mostly too different with respect to the extracted features to combine images of these two modalities for classifier training. Highlights: Using training data from di_erent imaging modalities can be bene_cial. Feature distributions are sigini_cantly di_erent for di_erent imaging modal-ties. Domain adaptation has only a small e_ect on the results.