In developing computerized systems for automatic diagnosis of cancer from images, it is very important to have a sufficiently large data set of training images, exhibiting the whole range of variation which can be observed in the different types of cancer. However obtaining such data sets is often difficult and impractical (even though huge volumes of raw data might be easily available) due mainly to the fact that obtaining labeled samples in a proper form for use in machine learning requires the costly time and effort of busy medical experts. The aim of this research project is to investigate methods which could make it possible to drastically reduce the number of required labeled training images for cancer diagnosis, while at the same time obtaining training data sets of very good quality, by using active learning and semi-supervised learning methods. These are being developed in a concrete setting, in the context of detection of colorectal cancer from Narrow Band Imaging (NBI) images obtained through colorectal endoscopy.