09.Machine learning07.Model transfer - sporedata/researchdesigneR GitHub Wiki
1. Use cases: in which situations should I use this method?
- When a previously existing image recognition model (segmentation, localization, and lesion pattern recognition such as grading) bears resemblence with a new model - see Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
2. Input: what kind of data does the method require?
- Labeled images
3. Algorithm: how does the method work?
Model mechanics
The most important step in transfer learning is hyperparameter optimization
Describing in words
Describing in images
Describing with code
Breaking down equations
Reporting guidelines
Data science packages
Suggested companion methods
Learning materials
- Books *
- Articles
4. Output: how do I interpret this method's results?
Mock conclusions or most frequent format for conclusions reached at the end of a typical analysis.
Tables, plots, and their interpretation
Tables and plots should compare traditional image recognition diagnostic tests between transfer and non-transfer models.