mlsanityData to 14 Oct 2026

torchmil: A PyTorch-based library for deep Multiple Instance Learning

arXiv 2509.08129 ↗First posted 9 Sep 2025cs.LGcited by 2

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Abstract (arXiv)

Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility and accessibility. To address this, we present torchmil, an open-source Python library built on PyTorch. torchmil offers a unified, modular, and extensible framework, featuring basic building blocks for MIL models, a standardized data format, and a curated collection of benchmark datasets and models. The library includes comprehensive documentation and tutorials to support both practitioners and researchers. torchmil aims to accelerate progress in MIL and lower the entry barrier for new users. Available at https://torchmil.readthedocs.io.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 9 Sep 2025.

Builds on

13 of 23 references.

References · 13

  1. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  2. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  3. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  4. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  5. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 150
  6. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  7. 2023Artificial Intelligence for Digital and Computational Pathologycited by 108
  8. 2022A graph-transformer for whole slide image classificationcited by 84
  9. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  10. 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
  11. 2023CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Imagescited by 13
  12. 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Testscited by 11
1 more
  1. 2024Sm: enhanced localization in Multiple Instance Learning for medical imaging classificationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.