Copyright
©The Author(s) 2021.
Artif Intell Gastroenterol. Dec 28, 2021; 2(6): 141-156
Published online Dec 28, 2021. doi: 10.35712/aig.v2.i6.141
Published online Dec 28, 2021. doi: 10.35712/aig.v2.i6.141
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 |
- Citation: Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156
- URL: https://www.wjgnet.com/2644-3236/full/v2/i6/141.htm
- DOI: https://dx.doi.org/10.35712/aig.v2.i6.141