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README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+library_name: pytorch
+tags:
+- medical-imaging
+- mri
+- 3d-resnet
+- monai
+- oncology
+- breast-cancer
+datasets:
+- odelia
+metrics:
+- roc_auc
+pipeline_tag: image-classification
+---
+
+# 3D ResNet-18 for ODELIA MRI Classification
+
+This repository contains the weights for a 3D ResNet-18 model trained for the ODELIA dataset (multi-parametric MRI). The model was developed to classify medical images into 3 distinct classes using 5-fold cross-validation.
+
+## Model Description
+
+- **Developed by:** THOUAN Simon
+- **Model type:** 3D Convolutional Neural Network (ResNet-18 architecture)
+- **Framework:** PyTorch & MONAI
+- **Task:** Multi-class 3D Image Classification (3 classes)
+- **Input:** 5 MRI sequences (Pre, Post_1, Post_2, Sub_1, T2) concatenated as channels.
+- **Input Size:** (128, 128, 64)
+
+## Architecture Details
+
+The model is a 3D adaptation of the ResNet architecture provided by the **MONAI** library:
+- **Blocks:** Basic block
+- **Layers:** [2, 2, 2, 2] (Equivalent to ResNet-18)
+- **In-planes:** [64, 128, 256, 512]
+- **Input Channels:** 5
+- **Spatial Dimensions:** 3D
+
+## Training Procedure
+
+The model was trained on a high-performance computing cluster (IDUN) using the following configuration:
+
+### Hyperparameters
+| Parameter | Value |
+| :--- | :--- |
+| **Optimizer** | Adam |
+| **Learning Rate** | 1e-4 |
+| **Loss Function** | CrossEntropyLoss |
+| **Batch Size** | 4 (Training) / 2 (Validation) |
+| **Epochs** | 50 |
+| **Validation** | Every 2 epochs |
+
+### Cross-Validation Strategy
+The dataset was split into 5 folds (A, B, C, D, E) to ensure robustness. 
+- **Fold 0:** Val = A, Train = B+C+D+E
+- **Fold 1:** Val = B, Train = A+C+D+E
+- *(Continuing for all 5 folds)*
+
+### Preprocessing (MONAI Transforms)
+- **Resizing:** All volumes resized to $128 \times 128 \times 64$.
+- **Normalization:** Intensity scaling for each sequence.
+- **Concatenation:** The 5 MRI sequences are stacked into a single 5-channel tensor.
+
+## Evaluation Results
+
+The primary metric used is **ROC AUC** (Area Under the Receiver Operating Analytic Curve). 
+Detailed performance graphs and confusion matrices for the ensemble can be found in the associated [GitHub Repository](URL_DE_TON_GITHUB).
+
+## How to Load the Model
+
+```python
+import torch
+from monai.networks.nets import ResNet
+
+# Initialize architecture
+model = ResNet(
+    block="basic", 
+    layers=[2, 2, 2, 2], 
+    block_inplanes=[64, 128, 256, 512],
+    n_input_channels=5, 
+    num_classes=3, 
+    spatial_dims=3
+)
+
+# Load weights
+state_dict = torch.load("weights/best_resnet_odelia_fold0.pth", map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+
+```
+
+## Limitations & Ethical Considerations
+This model is for research purposes only. It was trained on the ODELIA proprietary dataset. Predictions should not be used for clinical diagnosis without professional medical supervision.