Figure 4
From: Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake
Visualization of random forest classifier. (a) Binary decision tree for classifying FDG-avid tissues and tumor. Fifty binary decision trees were trained with the random forest classifier. After traversing the decision tree, tissue classification is decided and annotated. (TR: Tumor, KL: kidney left, BL: bladder, KR: kidney right, HT: heart, BR: brain). (b) Display of threshold values for volume and maximum SUV in all 50 trees. The color coding (red: bladder, green: brain, blue: heart, purple: kidney left, gold: kidney right, teal: tumor, white: undecided) shows the tissue classification made on two features: volume and centroid Z. (c) t-SNE plot illustrating the classification results for all segmented volumes and features using dimension reduction.
