Artificial intelligence in pathological evaluation of gastrointestinal cancers

Table 1 Strengths and weaknesses of machine learning methods in development of artificial intelligence models for gastrointestinal pathology

AI model	Advantages	Disadvantages
Traditional ML (supervised)	Allows users to produce a data output from the previously labeled training set	Labeling big data can be time-consuming and challenging
	Users can reflect domain knowledge features	Accuracy depends heavily on the quality of feature extraction
Traditional ML (unsupervised)	Users do not label any data or supervise the model	Input data is unknown and not labeled by users
	Can detect patterns automatically	Users cannot get precise information regarding data sorting
	Save time	Challenges during interpreting
CNN	Detects the important information and features without labeling	A large training data is required
	High performance in image recognition	Lack of interpretability (black boxes)
FCN	Provides computational speed	Requires large amounts of labeled data for training
	Automatically eliminates the background noise	High labeling cost
RNN	Can decide which information to remember from its past experience	Harder to train the model
	A deep learning model for sequential data	High computational cost
MIL	Does not require detailed annotation	A large amount of training data is required
	Can be applied to large data sets	High computational cost
GAN	Generates new realistic data resembling the original data	Harder to train the model

AI: Artificial intelligence; ML: Machine learning; CNN: Convolutional neural networks; FCN: Fully convolutional neural networks; RNN: Recurrent neural networks; MIL: Multi-instance learning; GAN: Generative adversarial networks.