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  Indian J Med Microbiol
 

Figure 5: Left: Performance plot for nucleus detection and classification via superpixels. Depicted are precision, recall, and F-score for the nucleus detection as well as sensitivity, specificity, and accuracy for the nucleus classification. Experiments were conducted with training set sizes from 5% to 100% (X-axis) of all nuclei in eight fully labeled TMA spots. Each box represents a leave-one-image-out cross validation run with a SVM (polynomial kernel). The performance stabilizes with 15% of training samples. The inter-expert performances of two pathologists is plotted last ("Pat"). For each of the eight images, pathologist A is taken as reference for pathologist's B guesses. Right: Proof of concept for the active learning approach in TMARKER. For three given TMA images, initially 10 malignant and 10 benign nuclei were selected to train an SVM. The classification result on all nuclei is shown as accuracy on the Y-axis. Consecutively, 20 additional nuclei were added repeatedly to the training (X-axis), thereby improving the classification performance. The additional nuclei are chosen at random ("acc ran") or systematically according to the respective lowest classification score ("acc sys"). The systematic approach saturates much faster. The classification accuracy reaches the level of the two pathologists ("acc pat")

Figure 5: Left: Performance plot for nucleus detection and classification via superpixels. Depicted are precision, recall, and F-score for the nucleus detection as well as sensitivity, specificity, and accuracy for the nucleus classification. Experiments were conducted with training set sizes from 5% to 100% (X-axis) of all nuclei in eight fully labeled TMA spots. Each box represents a leave-one-image-out cross validation run with a SVM (polynomial kernel). The performance stabilizes with 15% of training samples. The inter-expert performances of two pathologists is plotted last (