A CNN is applied to a histological image to identify areas of interest. The CNN classifies pixels according to relevance classes including one or more classes indicating levels of interest and at least one class indicating lack of interest. The CNN is trained on a training
data set including data which has recorded how pathologists have interacted with visualizations of histological images. In the trained CNN, the interest-based
pixel classification is used to generate a segmentation
mask that defines areas of interest. The
mask can be used to indicate where in an image clinically relevant features may be located. Further, it can be used to guide variable
data compression of the histological image. Moreover, it can be used to control loading of image data in either a
client-
server model or within a memory cache policy. Furthermore, a histological image of a
tissue sample of a
tissue type that has been treated with a test compound is image processed in order to detect areas where toxic reactions to the test compound may have occurred. An
autoencoder is trained with a training
data set comprising histological images of tissue samples which are of the given
tissue type, but which have not been treated with the test compound. The trained
autoencoder is applied to detect tissue areas by their deviation from the
normal variation seen in that
tissue type as learnt by the training process, and so build up a
toxicity map of the image. The
toxicity map can then be used to direct a toxicological pathologist to examine the areas identified by the
autoencoder as
lying outside the
normal range of heterogeneity for the tissue type. This makes the pathologists review quicker and more reliable. The
toxicity map can also be overlayed with the segmentation
mask indicating areas of interest. When an area of interest and an area identified as
lying outside the
normal range of heterogeneity for the tissue type, and increased
confidence score is applied to the overlapping area.