---
license: cc-by-nc-sa-4.0
---

# RetCCL-based ABMIL models for metastasis detection

These are weakly-supervised, attention-based multiple instance learning models for binary metastasis detection (normal versus metastasis). The models were trained on the [CAMELYON16](https://camelyon16.grand-challenge.org/Data/) dataset using RetCCL embeddings.

# Data

- Training set consisted of 243 whole slide images (WSIs).
    - 143 negative
    - 100 positive
        - 52 macrometastases
        - 48 micrometastases
- Validation set consisted of 27 WSIs.
    - 16 negative
    - 11 positive
        - 6 macrometastases
        - 5 micrometastases
- Test set consisted of 129 WSIs.
    - 80 negative
    - 49 positive
        - 22 macrometastases
        - 27 micrometastases

# Evaluation

Below are the classification results on the test set.

|   Seed |   Sensitivity |   Specificity |    BA |   Precision |    F1 |
|-------:|--------------:|--------------:|------:|------------:|------:|
|  0 |         0.612 |         0.838 | 0.725 |       0.698 | 0.652 |
|  1 |         0.531 |         0.950 | 0.740 |       0.867 | 0.658 |
|  2 |         0.551 |         0.950 | 0.751 |       0.871 | 0.675 |
|  3 |         0.612 |         0.925 | 0.769 |       0.833 | 0.706 |
|  4 |         0.531 |         0.950 | 0.740 |       0.867 | 0.658 |

# How to reuse the model

The model expects 128 x 128 micrometer patches, embedded with the RetCCL model.

```python
import torch
from abmil import AttentionMILModel

model = AttentionMILModel(in_features=2048, L=512, D=384, num_classes=2, gated_attention=True)
model.eval()
state_dict = torch.load("seed3/model_best.pt", map_location="cpu", weights_only=True)
model.load_state_dict(state_dict)

# Load a bag of features
bag = torch.ones(1000, 2048)
with torch.inference_mode():
    logits, attention = model(bag)
```