Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets

Recent work on computer vision and image processing has relied substantially on open datasets, which allow for an objective comparison of techniques and methodologies. In the area of computational pathology and, more specifically, on colorectal cancer, the dataset NCT-CRC-HE-100K, which consists of 100,000 patches of human tissue stained with Haematoxylin and Eosin has been widely used as a training set for deep learning studies. The patches are grouped into 9 classes of tissue (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, colorectal adenocarcinoma epithelium). The set is released with a separate set (CRC-VAL-HE-7K) of 7,180 patches that is commonly used for testing. In this work, features were extracted from both sets first with Persistent Homology, then, with Gabor filters to reveal that the training set presents a rather different distribution from the testing set. Namely, the distribution of features in the 7K-set presents a much higher class overlap than those in the 100K-set, which would imply a much higher separability in the testing set than in the training set.