A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality, staining quality, and tissue appearance, before segmenting nuclei and finally classifying or

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... g areas of tissue. The key step is the training of a classifier to judge the nuclei segmentation quality. Using active learning techniques, we train this classifier to identify problems in the image as well as weaknesses of the image analysis tools. This way we obtain robust nuclear segmentation allowing precise measurements of features that can be used safely for classification. We validate our findings on several hundred cases of breast cancer, demonstrating that automatic pleomorphism grading is possible with high accuracy. This technique can provide a stable and objective basis for what has been a subjective process that suffers from low reproducibility.