AMLab-Amsterdam/AttentionDeepMIL

Health v2 · maintenance only

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Adoption not part of the score

1012 stars · 205 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the paper 'Attention-based Deep Multiple Instance Learning' (Ilse, Tomczak & Welling, 2018). It provides code to run the MNIST-BAGS experiment and includes a modified LeNet-5 model with attention-based MIL pooling.

Use cases

• reproduce the attention-based deep MIL paper results
• run the MNIST-BAGS experiment in PyTorch
• learn how attention-based MIL pooling works
• apply multiple instance learning to bag-of-instances classification
• adapt MIL models for histopathology image classification

When to choose

• you need a reference implementation of attention-based MIL
• you want to reproduce the MNIST-BAGS benchmark from the paper
• you are researching multiple instance learning with attention

When to avoid

• you need a production-ready MIL framework with validation and early stopping
• you need large mean bag lengths or balanced training data
• you need active support or maintenance guarantees

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python multiple-instance-learning attention-mechanism pytorch research-code mnist-bags research gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="AMLab-Amsterdam/AttentionDeepMIL")