Torchmil: A PyTorch-based library for deep multiple instance learning
Metadatos
Afficher la notice complèteAuteur
Castro Macías, Francisco M.; Sáez Maldonado, Francisco J.; Morales Álvarez, Pablo; Molina Soriano, RafaelEditorial
Elsevier
Materia
Multiple instance learning Machine learning Deep Learning
Date
2026-06-01Referencia bibliográfica
Castro-Macías, F. M., Sáez-Maldonado, F. J., Morales-Álvarez, P., & Molina, R. (2026). Torchmil: A PyTorch-based library for deep multiple instance learning. Neurocomputing, 680(133286), 133286. https://doi.org/10.1016/j.neucom.2026.133286
Patrocinador
MCIN / AEI / 10.13039 / 501100011033 - (PID2022-140189OB-C22); Ministerio de Universidades - (FPU21/01874); Consejería de Universidad, Investigación e Innovación and by the European Union (EU) ERDF Andalusia Program 2021–2027 - (C-EXP-153-UGR23); Universidad de Granada/CBUA - (Open access charge)Résumé
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.





