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Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
dc.contributor.author | Castro Macías, Francisco M. | |
dc.contributor.author | Morales Álvarez, Pablo | |
dc.contributor.author | Wu, Yunan | |
dc.contributor.author | Molina Soriano, Rafael | |
dc.contributor.author | Katsaggelos, Aggelos K. | |
dc.date.accessioned | 2024-07-02T09:33:10Z | |
dc.date.available | 2024-07-02T09:33:10Z | |
dc.date.issued | 2024-03-15 | |
dc.identifier.citation | 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] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/92918 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | Project PID2019-105142RB-C22 | es_ES |
dc.description.sponsorship | Project PID2022-140189OB-C22 | es_ES |
dc.description.sponsorship | Project B-TIC-324-UGR20 | es_ES |
dc.description.sponsorship | Ministerio de Universidades under FPU contract FPU21/01874 | es_ES |
dc.description.sponsorship | C-EXP-153-UGR23 funded by Consejería de Universidad, Investigación e Innovación and by ERDF Andalusia Program 2021-2027 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Multiple Instance Learning | es_ES |
dc.subject | Gaussian processes | es_ES |
dc.subject | Jaakkola bound | es_ES |
dc.title | Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1016/j.artint.2024.104115 | |
dc.type.hasVersion | VoR | es_ES |