# Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images
## 4 Discussion  
The development of new methods for diagnosing colorectal cancer burdens pathologists with the work of segmentation. Deep learning has been successfully applied in computational pathology in the past few years to automate this task. The computer program can process large amounts of WSI automatically without feeling fatigue, thus becoming a useful AI assistant for pathologists. The application of deep learning in the diagnosis of colorectal or other types of cancer has many advantages, such as the high speed of diagnosis. However, it has some limitations as well. Reliable identification of colon tumors (Section 2.1) requires a similar amount of WSI for each type. This is quite difficult to provide, due to the rare occurrence of certain diseases. Even for a human specialist, the correct definition of the cancer is quite a difficult task. The problem of differentiation between six types of lesions solved in this work is more complex, compared to the common binary classification of histological images. This explains lower values of the area under ROC curves, compared to the most published works [19; 24; 25; 29].  
The closest results were obtained recently by [PERSON] and [PERSON] [37]. Using the database of 1799 WSI, they performed binary classification to detect the colorectal poorly differentiated adenocarcinoma. Using the same transfer learning, the authors managed to achieve a receiver operating characteristic curve-area under the curves of up to 0.95. A strict comparison of this result with our 0.91 is inappropriate, due to different database statistics, but a rough comparison is very useful. The test database of [PERSON] and [PERSON] consists of 74 WSIs with a poorly differentiated ADC diagnosis, 404 moderately differentiated ADCs, 643 adenoma, and 678 non-neoplastic subsets. The authors focused on the problem of poorly differentiated ADC discrimination. This class diagnosis is rare and requires special treatment.  
Due to the rarity of the poorly differentiated ADCs, our dataset contained only 21 such cases out of 1785 WSI. As we split the whole database into train 60%, validation 20%, and test 20% parts, the last one contained only four WSIs with poorly differentiated ADCs. Thus, the WSI-level error varied a lot during random sampling. The patch-level error was more statistically stable but differed from the WSI-level value as much as the micro-average score differed from the macro-averaged score.  
Figure 9: Examples of CNN predictions. The patches were painted when the predicted class probability was greater than 0.5.  
Our study clearly showed the complexity of differential diagnosis between the structures of tubular adenoma, villous adenoma, and well-differentiated adenocarcinoma in conditions of insufficient data of these classes. The initial growth of highly differentiated adenocarcinoma often occurs in tubular and villous adenoma, and when pathologists label these H- and E-stained structures and face them with transition areas, they assign an uncertain class. Other structures do not cause an ambiguous interpretation by different specialists. This issue could be solved by multiple validation of the same WSI labelling by several specialists.  
Another weakness of the neural network in cancer diagnosis is its subjective predictions. The neural network is trained on images labeled by a team of pathologists, and thus, it will produce results similar to those of doctors. To achieve complete objectivity in cancer diagnosis, data should be obtained with various equipment and labeled by different groups of pathologists.