Artificial intelligence in cystoscopic bladder cancer classification based on transfer learning with a pre-trained convolutional neural network without natural images

Ryuunosuke Kounosu; Wonjik Kim; Atsushi Ikeda; Hirokazu Nosato; Yuu Nakajima

doi:10.1117/12.3005605

3 April 2024 Artificial intelligence in cystoscopic bladder cancer classification based on transfer learning with a pre-trained convolutional neural network without natural images

Ryuunosuke Kounosu, Wonjik Kim, Atsushi Ikeda, Hirokazu Nosato, Yuu Nakajima

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292727 (2024) https://doi.org/10.1117/12.3005605
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Artificial intelligence (AI) systems for diagnostic assistance in medical imaging are being researched and developed for main diagnostic imaging areas, such as magnetic resonance imaging and computed tomography imaging. These fields have a large number of examinations, and sufficient training data can be obtained for AI training. However, it may be challenging to collect sufficient training data to train AI in some minor areas, such as cystoscopy. In such cases, pre-trained AI models, which are pre-trained on a large, general-purpose image database based on real images, are often used. However, such large image databases containing real images are subject to copyright issues because the images are collected from the Internet, and mislabeling issues arise because annotation is performed manually. When building AI for medical images, the transparency of the training data is more important, and such problems are undesirable for the pre-training data used to build AI for diagnostic support. Therefore, this study proposed a new pre-training method based on automatically generated image databases to train the AI as a pre-training method that does not rely on real images in developing a diagnostic AI system for cystoscopic images. The objective was to build a diagnostic support system with a classification performance equivalent to that of expert urologists. Proposed method mixed two types of formula-driven image databases with the texture and contour features inspired by cystoscopic images. In the conducted experiments, the effectiveness of proposed method was verified for the classification of cystoscopic images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ryuunosuke Kounosu, Wonjik Kim, Atsushi Ikeda, Hirokazu Nosato, and Yuu Nakajima "Artificial intelligence in cystoscopic bladder cancer classification based on transfer learning with a pre-trained convolutional neural network without natural images", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292727 (3 April 2024); https://doi.org/10.1117/12.3005605

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