@misc{10481/106026,
year = {2025},
month = {7},
url = {https://hdl.handle.net/10481/106026},
abstract = {In the context of histological image classification, Multiple Instance Learning (MIL) methods
only require labels at Whole Slide Image (WSI) level, effectively reducing the annotation bottleneck.
However, for their deployment in real scenarios, they must be able to detect the presence of previously
unseen tissues or artifacts, the so-called Out-of-Distribution (OOD) samples. This would allow Computer
Assisted Diagnosis systems to flag samples for additional quality or content control. In this work, we propose
an OOD-aware probabilistic deep MIL model that combines the latent representation from a variational
autoencoder and an attention mechanism. At test time, the latent representations of the instances are used
in the classification and OOD detection tasks. We also propose a deterministic version of the model that
uses the reconstruction error as OOD score. Panda (prostate tissue) and Camelyon16 (lymph node tissue) are
used as train/test in-distribution datasets, obtaining bag classification results competitive with current stateof-the-art models. OOD detection is evaluated performing two experiments for each in-distribution dataset.
For Panda, Camelyon16 and ARTIF (prostate tissue contaminated with artifacts) are used as OOD datasets,
obtaining 100% AUC in both cases. For Camelyon16, Panda and BCELL (lymph node tissue diagnosed with
diffuse large B-cell lymphoma) are used as OOD datasets, obtaining AUCs of 100% and 97%, respectively.
Experimental validation demonstrates the models’ strong classification performance and effective OOD slide
detection, highlighting their clinical potential.},
organization = {MICIU/AEI/10.13039/501100011033 and by NextGenerationEU/PRTR (PID2022-140189OB-C22, TED2021-132178B-I00)},
organization = {U.S. National Institutes of Health National Library of Medicine Award (Grant R01LM013523)},
organization = {U.S. National Institutes of Health (Grant K08EB030120)},
publisher = {IEEE},
keywords = {Out-of-distribution detection},
keywords = {Multiple instance learning},
keywords = {Variational Autoencoder},
title = {Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning},
doi = {10.1109/access.2025.3593420},
author = {Sáez-Maldonado, Francisco Javier and García Martínez, María Luz and Cooper, Lee A.D. and Goldstein, Jeffery A. and Molina Soriano, Rafael and Katsaggelos, Aggelos K.},
}