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README.md

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 license: cc-by-4.0
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+# Metastasis classification (CAMELYON16)
+
+This model classifies an H&E-stained digital pathology image of axillary lymph nodes as not containing metastasis or containing metastasis. It was trained by Jakub Kaczmarzyk.
+
+Inputs: Bag of patches with 128um edge length, embedded with CTransPath.
+
+Output classes: no-metastasis, metastasis
+
+## Data
+
+CAMELYON16 was used to train the model. The whole slide images were tiled into 128x128um patches, and each patch was encoded using CTransPath (this produces 768-dimensional embeddings).
+
+Train and validation splits were stratified by metastasis status. The test set is pre-defined in the CAMELYON16 dataset.
+
+Samples sizes:
+- Train: 243 slides
+- Validation: 27 slides
+- Test: 129 slides
+
+## Reusing this model
+
+To use this model on the command line, see [WSInfer-MIL](https://github.com/kaczmarj/wsinfer-mil).
+
+Alternatively, you may use PyTorch on ONNX to run the model. First, embed 128um x 128um patches using CTransPath. Then pass the bag of embeddings to the model.
+
+```python
+import onnxruntime as ort
+import numpy as np
+embedding = np.ones((1_000, 768), dtype="float32")
+ort_sess = ort.InferenceSession("model.onnx")
+logits, attention = ort_sess.run(["logits", "attention"], {'input': embedding})
+```
+
+## Model performance
+
+The model achieves an AUC of 0.91 in the test set of CAMELYON16. Below, please find a confusion matrix, where predicted classes are columns, and true values are rows.
+
+|        | No-Met | Met |
+|--------|--------|-----|
+| No-Met | 77     | 3   |
+| Met    | 12     | 37  |
+
+# Intended uses
+
+This model is ONLY intended for research purposes.
+
+**This model may not be used for clinical purposes.** This model is distributed without warranties, either express or implied.

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