@misc{10481/86120,
year = {2023},
month = {10},
url = {https://hdl.handle.net/10481/86120},
abstract = {In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational pathology, where the lack of patch-level labels for whole-slide images prevents the application of supervised models. Probabilistic MIL methods based on Gaussian Processes (GPs) have obtained promising results due to their excellent uncertainty estimation capabilities. However, these are general-purpose MIL methods that do not take into account one important fact: in (histopathological) images, the labels of neighboring patches are expected to be correlated. In this work, we extend a state-of-the-art GP-based MIL method, which is called VGPMIL-PR, to exploit such correlation. To do so, we develop a novel coupling term inspired by the statistical physics Ising model. We use variational inference to estimate all the model parameters. Interestingly, the VGPMIL-PR formulation is recovered when the weight that regulates the strength of the Ising term vanishes. The performance of the proposed method is assessed in two real-world problems of prostate cancer detection. We show that our model achieves better results than other state-of-the-art probabilistic MIL methods. We also provide different visualizations and analysis to gain insights into the influence of the novel Ising term. These insights are expected to facilitate the application of the proposed model to other research areas.},
organization = {European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska
Curie grant agreement No 860627 (CLARIFY Project)},
organization = {Spanish
Ministry of Science and Innovation under project PID2019-105142RBC22},
organization = {University of Granada and FEDER/Junta de Andalucía
under project B-TIC-324-UGR20 (Proyectos de I+D+i en el
marco del Programa Operativo FEDER Andalucía)},
organization = {Margarita Salas postdoctoral fellowship (Spanish Ministry
of Universities with Next Generation EU funds)},
publisher = {Elsevier},
keywords = {Multiple instance learning},
keywords = {Gaussian Processes},
keywords = {Ising model},
keywords = {Variational inference},
keywords = {Whole slide imaging},
keywords = {Histopathology},
title = {Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images},
doi = {10.1016/j.patcog.2023.110057},
author = {Morales Álvarez, Pablo and Schmidt, Arne and Hernández-Lobato, José Miguel and Molina Soriano, Rafael},
}