| --- |
| license: other |
| license_name: mixed-source-licenses |
| license_link: https://github.com/injurise/ICMIL/blob/main/THIRD_PARTY.md |
| pretty_name: ICMIL Benchmarks |
| task_categories: |
| - tabular-classification |
| tags: |
| - multiple-instance-learning |
| - benchmark |
| --- |
| |
| # ICMIL Benchmark Datasets |
|
|
| 📄 [Paper (arXiv:2606.06458)](https://arxiv.org/abs/2606.06458) · 💻 [Code](https://github.com/injurise/ICMIL) · 🤗 [Model](https://huggingface.co/bifold-pathomics/ICMIL) |
|
|
| The twelve multiple-instance learning (MIL) benchmarks used to evaluate |
| [ICMIL](https://huggingface.co/bifold-pathomics/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: |
|
|
| ```python |
| 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](https://archive.ics.uci.edu/dataset/74/musk+version+1) / [v2](https://archive.ics.uci.edu/dataset/75/musk+version+2), [Letter Recognition](https://archive.ics.uci.edu/dataset/59/letter+recognition), [HEPMASS](https://archive.ics.uci.edu/dataset/347/hepmass) | CC BY 4.0 | |
| | `mnist_xai_benchmark_100bags.h5` | [MNIST](http://yann.lecun.com/exdb/mnist/) | CC BY-SA 3.0 | |
| | `andrews_mil_benchmark.h5` | [Fox / Tiger / Elephant](https://www.cs.columbia.edu/~andrews/mil/datasets.html) image bags | none stated | |
| | `tcga_uni2_luad_vs_lusc.h5` | TCGA LUAD/LUSC slides, [UNI2-h](https://huggingface.co/MahmoodLab/UNI2-h) features | TCGA open access; UNI2-h CC BY-NC-ND 4.0 | |
| | `rsna_ich_resnet50_draws_100bags.h5` | [RSNA-ICH](https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection), via [torchmil](https://huggingface.co/datasets/torchmil/RSNA_ICH_MIL) | RSNA challenge terms | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
