[2K5-OS-1a] 医療におけるAIの社会実装に向けて(1/2)

[2K5-OS-1a-04] Support system for Narrow-band imaging cystoscopy based on small-scale data learning in DCNN

〇Shogo Takaoka^1,2, Atsushi Ikeda³, Hirokazu Nosato¹, Hidenori Sakanashi^1,2, Masahiro Murakawa^1,2 (1. National Institute of Advanced Industrial Science and Technology(AIST), 2. University of Tsukuba, 3. University of Tsukuba Hospital)

Keywords:Deep convolutional neural network, cystoscopy, Variational Autoencoder, small-scale data learning, annotation extension

This paper proposes a lesion detection model using annotation expansion for Narrow Band Imaging (NBI). In the proposed method, Variational Autoencoder (VAE) is used to extend the annotation of a data-set performed by medical specialists. This method compensates for the lack of NBI data, which is not easy to collect, and leads to improved lesion detection performance. In this paper, in order to verify the effectiveness of the proposed method, experiments using actual bladder endoscopic images were performed. As a result of the experiment, a sensitivity of 74.9%, specificity of 98.2%, and F value of 78.0% were obtained. This result shows an improvement in lesion detection performance compared to the model without annotation expansion, and confirms the effectiveness of the proposed method for lesion detection.