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Check out the documentation for more information.
This repository contains several trained model variants used for prostate cancer recurrence prediction. Please see GitHub link for further details:
Each model was trained for 10 epochs per fold in a 5-fold cross-validation setup.
For every fold, the best-performing checkpoint based on validation C-index was saved.
All model files follow this naming pattern: _foldX.pt, where X denotes the fold (0-4)
Available Models
1. Clinical Linear Model
Description: Regularized Cox proportional hazards model using clinical data
Prefix: clinical_linear_model
2. Clinical MLP
Description: Fully connected neural network using clinical data. Learning rate 1e-3
Prefix: clinical_mlp_model
3. MRI CNN
Description: 3D CNN trained on full MRI volumes (ADC, HBV, T2W + mask channel). Preprocessing includes mask as a seperate channel
Prefix: mri_cnn_model
4. Full Multimodal Model
Description: Multimodal model integrating Clinical MLP backbone to process clinical data and MRI CNN backbone to process MRI volumes. Feature embeddings from each branch passed through a risk prediction MLP
Prefix: multimodal_model_full
5. Multimodal Model — MRI Ablated
Description: Multimodal model with MRI CNN branch ablated
Prefix: mutlimodal_model_mri_ablated
6. Multimodal Model — Clinical Ablated
Description: Multimodal model with Clinical MLP branch ablated
Prefix: mutlimodal_model_clinical_ablated
7. Clinical MLP - Learning Rate 1e-4
Description: Fully connected neural network using clinical data (Same as #2) but with a Learning rate 1e-4
Prefix: clinical_mlp_model_lr1e_4
8. MRI CNN - Precropped
Description: 3D CNN trained on full MRI volumes (ADC, HBV, T2W). However, channels cropped using T2W-mask prior to concatinating into 1 tensor.
Prefix: mri_cnn_model_precropped
⭐ 9. Full Multimodal Model (Dual Learning Rate Optimizer)
Description: Same as #4 but uses a dual learning-rate optimizer (higher LR for the clinical MLP, lower LR for the MRI CNN) to balance learning between modalities.
The strongest-performing variant across all folds
prefix: multimodal_model_full_diff_lr