Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset.

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ID: 104853
2019
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Abstract
This Letter presents a stable polyp-scene classification method with low false positive (FP) detection. Precise automated polyp detection during colonoscopies is essential for preventing colon-cancer deaths. There is, therefore, a demand for a computer-assisted diagnosis (CAD) system for colonoscopies to assist colonoscopists. A high-performance CAD system with spatiotemporal feature extraction via a three-dimensional convolutional neural network (3D CNN) with a limited dataset achieved about 80% detection accuracy in actual colonoscopic videos. Consequently, further improvement of a 3D CNN with larger training data is feasible. However, the ratio between polyp and non-polyp scenes is quite imbalanced in a large colonoscopic video dataset. This imbalance leads to unstable polyp detection. To circumvent this, the authors propose an efficient and balanced learning technique for deep residual learning. The authors' method randomly selects a subset of non-polyp scenes whose number is the same number of still images of polyp scenes at the beginning of each epoch of learning. Furthermore, they introduce post-processing for stable polyp-scene classification. This post-processing reduces the FPs that occur in the practical application of polyp-scene classification. They evaluate several residual networks with a large polyp-detection dataset consisting of 1027 colonoscopic videos. In the scene-level evaluation, their proposed method achieves stable polyp-scene classification with 0.86 sensitivity and 0.97 specificity.
Reference Key
itoh2019stablehealthcare Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Itoh, Hayato;Roth, Holger;Oda, Masahiro;Misawa, Masashi;Mori, Yuichi;Kudo, Shin-Ei;Mori, Kensaku;
Journal healthcare technology letters
Year 2019
DOI
10.1049/htl.2019.0079
URL
Keywords
Cancer image classification feature extraction 3d cnn learning (artificial intelligence) medical image processing computerised tomography biological organs colonoscopic video dataset computer-assisted diagnosis system convolutional neural nets endoscopes false positive detection high-performance cad system imbalanced large dataset nonpolyp scenes polyp-detection dataset residual learning stable polyp-scene classification method subsampling three-dimensional convolutional neural network unstable polyp detection

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