Medical Recommender System for image classification without retraining
Traditional ML
- Support Vector Machine (SVM)
- Decision Tree (DT)
- Random Forest (RF)
- XGBoost
Deep leaning
- CNN
- LSTM and GRU
- Capsule Net
- Transfer learning (ResNet, DenseNet, VGG16,...)
- Transformer
- Swin Transformer
Create MedicalRec dataset
| Ref | #Sample | #Train | #Test | #Validation | K-fold | Width | hight | #channel | #Class | Domain | Accu | Pre | Rec | F1 | AUC | Model |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [1] | 10000 | 7000 | 20000 | 10000 | Yes | 255 | 255 | 3 | 2 | Tumor Classification | 0.91 | 0.90 | 0.93 | 0.92 | 0.92 | SVM |
Write dataset report
Train Transformer recommander model
Testing in real data
Save weights and public code
COLLABORATION WITH: MedicalRec


