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license: cc-by-nc-sa-4.0 |
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# Phikon-based ABMIL models for metastasis detection |
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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 Phikon embeddings. |
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# Data |
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- Training set consisted of 243 whole slide images (WSIs). |
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- 143 negative |
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- 100 positive |
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- 52 macrometastases |
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- 48 micrometastases |
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- Validation set consisted of 27 WSIs. |
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- 16 negative |
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- 11 positive |
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- 6 macrometastases |
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- 5 micrometastases |
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- Test set consisted of 129 WSIs. |
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- 80 negative |
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- 49 positive |
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- 22 macrometastases |
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- 27 micrometastases |
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# Evaluation |
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Below are the classification results on the test set. |
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| Seed | Sensitivity | Specificity | BA | Precision | F1 | |
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|-------:|--------------:|--------------:|------:|------------:|------:| |
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| 0 | 0.939 | 0.963 | 0.951 | 0.939 | 0.939 | |
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| 1 | 0.571 | 0.950 | 0.761 | 0.875 | 0.691 | |
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| 2 | 0.857 | 1.000 | 0.929 | 1.000 | 0.923 | |
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| 3 | 0.898 | 0.988 | 0.943 | 0.978 | 0.936 | |
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| 4 | 0.959 | 0.950 | 0.955 | 0.922 | 0.940 | |
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# How to reuse the model |
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The model expects 128 x 128 micrometer patches, embedded with the Phikon model. |
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```python |
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import torch |
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from abmil import AttentionMILModel |
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model = AttentionMILModel(in_features=768, L=512, D=384, num_classes=2, gated_attention=True) |
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model.eval() |
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state_dict = torch.load("seed4/model_best.pt", map_location="cpu", weights_only=True) |
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model.load_state_dict(state_dict) |
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# Load a bag of features |
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bag = torch.ones(1000, 768) |
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with torch.inference_mode(): |
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logits, attention = model(bag) |
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``` |
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