Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
Metadatos
Afficher la notice complèteAuteur
Castro Macías, Francisco M.; Morales Álvarez, Pablo; Wu, Yunan; Molina Soriano, Rafael; Katsaggelos, Aggelos K.Editorial
Elsevier
Materia
Multiple Instance Learning Gaussian processes Jaakkola bound
Date
2024-03-15Referencia bibliográfica
F.M. Castro-Macías, P. Morales-Álvarez, Y. Wu et al. Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection. Artificial Intelligence 331 (2024) 104115. [https://doi.org/10.1016/j.artint.2024.104115]
Patrocinador
Project PID2019-105142RB-C22; Project PID2022-140189OB-C22; Project B-TIC-324-UGR20; Ministerio de Universidades under FPU contract FPU21/01874; C-EXP-153-UGR23 funded by Consejería de Universidad, Investigación e Innovación and by ERDF Andalusia Program 2021-2027Résumé
Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully
applied to many different scientific areas and is particularly well suited to medical imaging.
Probabilistic MIL methods, and more specifically Gaussian Processes (GPs), have achieved
excellent results due to their high expressiveness and uncertainty quantification capabilities.
One of the most successful GP-based MIL methods, VGPMIL, resorts to a variational bound
to handle the intractability of the logistic function. Here, we formulate VGPMIL using Pólya-
Gamma random variables. This approach yields the same variational posterior approximations
as the original VGPMIL, which is a consequence of the two representations that the Hyperbolic
Secant distribution admits. This leads us to propose a general GP-based MIL method that takes
different forms by simply leveraging distributions other than the Hyperbolic Secant one. Using the
Gamma distribution we arrive at a new approach that obtains competitive or superior predictive
performance and efficiency. This is validated in a comprehensive experimental study including
one synthetic MIL dataset, two well-known MIL benchmarks, and a real-world medical problem.
We expect that this work provides useful ideas beyond MIL that can foster further research in the
field.