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Phikon-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 dataset using Phikon 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 0.939 0.963 0.951 0.939 0.939
1 0.571 0.950 0.761 0.875 0.691
2 0.857 1.000 0.929 1.000 0.923
3 0.898 0.988 0.943 0.978 0.936
4 0.959 0.950 0.955 0.922 0.940

How to reuse the model

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

import torch
from abmil import AttentionMILModel

model = AttentionMILModel(in_features=768, L=512, D=384, num_classes=2, gated_attention=True)
model.eval()
state_dict = torch.load("seed4/model_best.pt", map_location="cpu", weights_only=True)
model.load_state_dict(state_dict)

# Load a bag of features
bag = torch.ones(1000, 768)
with torch.inference_mode():
    logits, attention = model(bag)