ICMIL Benchmark Datasets

📄 Paper (arXiv:2606.06458) · 💻 Code · 🤗 Model

The twelve multiple-instance learning (MIL) benchmarks used to evaluate ICMIL and the baselines. ICMIL is an in-context learner that labels new bags of instances from a handful of labelled bags in a single forward pass, without task-specific training. The train/test splits here are fixed, so numbers from different methods are comparable.

Files

File	Benchmark tasks	Source features
uci_benchmark.h5	uci_musk1, uci_musk2, uci_letters, uci_hepmass	UCI raw attributes, PCA to 25 dims (16 for letters)
mnist_xai_benchmark_100bags.h5	mnist_xai_smil, mnist_xai_pos_neg, mnist_xai_adjacent_pairs, mnist_xai_four_bags	MNIST digits, ResNet-18 embeddings, PCA to 25
andrews_mil_benchmark.h5	andrews_fox, andrews_tiger, andrews_elephant	Blobworld region descriptors (230 dims), PCA to 25
tcga_uni2_luad_vs_lusc.h5	tcga_fixed	TCGA WSI patches, UNI2 features (1536 dims), PCA to 25
rsna_ich_resnet50_draws_100bags.h5	rsna_ich_draws	RSNA-ICH CT slices, ResNet-50 features (2048 dims), PCA to 25

Getting started

Each file is an HDF5 file with the train/test splits already made, so you can load a benchmark and start straight away:

import h5py

with h5py.File("uci_benchmark.h5") as f:
    g = f["musk1"]["split_0"]
    X_train, y_train = g["X_train"][:], g["y_train"][:]
    X_test, y_test = g["X_test"][:], g["y_test"][:]

X_train.shape   # (n_bags, bag_size, n_features)
y_train.shape   # (n_bags,) — one label per bag

Finding the splits inside a file:

• They are named split_0, split_1, … where the split is fixed, and draw_0, draw_1, … where it is one of several random draws.
• In the UCI, Andrews and MNIST-XAI files each task has its own group, so a split lives at e.g. musk1/split_0. The TCGA and RSNA files each hold a single task, so the splits sit at the top level: split_0, draw_0.
• A file's attributes say which feature extractor and split settings were used.

Licensing

File	Source	Licence
uci_benchmark.h5	UCI Musk v1 / v2, Letter Recognition, HEPMASS	CC BY 4.0
mnist_xai_benchmark_100bags.h5	MNIST	CC BY-SA 3.0
andrews_mil_benchmark.h5	Fox / Tiger / Elephant image bags	none stated
tcga_uni2_luad_vs_lusc.h5	TCGA LUAD/LUSC slides, UNI2-h features	TCGA open access; UNI2-h CC BY-NC-ND 4.0
rsna_ich_resnet50_draws_100bags.h5	RSNA-ICH, via torchmil	RSNA challenge terms

Citation

@article{mollers2026incontext,
  title   = {In-Context Multiple Instance Learning},
  author  = {M\"ollers, Alexander and Sextro, Marvin and Hense, Julius and Dernbach, Gabriel and M\"uller, Klaus-Robert},
  journal = {arXiv preprint arXiv:2606.06458},
  year    = {2026}
}