2015 Fiscal Year Annual Research Report

Active Learningを用いた大腸癌自動診断システム

Research Project

Project/Area Number	25330337
Research Institution	Hiroshima University
Principal Investigator	ライチェフビセル広島大学, 工学(系)研究科(研究院), 助教 (00531922)
Project Period (FY)	2013-04-01 – 2016-03-31
Keywords	癌の自動診断システム / 大腸癌 / NBI画像 / Active learning / Ensemble methods
Outline of Annual Research Achievements	An automatic diagnosis method for colorectal cancer from Narrow Band Imaging (NBI) has been developed, based on local context features and randomized decision forests. The local context features are based on a texton map from which texture and local context-based information is extracted from the surrounding area centered on each pixel. The features are very high-dimensional (infinite in principle) and therefore very discriminative, which combined with their huge number and the ability of random forests to handle efficiently such data without over-fitting enables us to achieve very good accuracy from a very small number of training images. Additionally, by providing local pixel-level classification the resulting method is much more general and does not depend on the concrete configuration of patterns available in the training images. The method operates locally and therefore is much better suited for video data also, which makes it applicable to more realistic diagnostic scenarios.