Fig. 2

The segmentation workflow. Similar to classification workflow, square patches of 112 pixels in size are sampled on a rectangular grid with 8-pixel stride. Each patch is assigned a positive (orange) or negative (blue) label, which are necrosis vs. non-necrosis in brain tumor, and cancer vs. normal in colon cancer, respectively. In training phase, a patch is labelled positive if its overlap ratio with annotated segmented region is larger than 0.6. Patches are then resized and a 4096-dimensional feature vector is extracted from our CNN model. A linear SVM classifier is used to distinguish negative from positive patches. Probability mapping images are yielded utilizing all predicted confidence scores. After smoothing, positive segmentations are obtained