Automatic detection of colorectal cancer from images using active learning

2015 Fiscal Year Final Research Report

• Project/Area Number: 25330337
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Life / Health / Medical informatics
• Research Institution: Hiroshima University
• Principal Investigator: Raytchev Bisser 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: 大腸癌 / 癌の自動診断システム / ＮＢＩ画像 / Active Learning / Ensemble methods

Outline of Final 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.

Free Research Field

情報学・機械学習