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[HTML][HTML] Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

FM Castro-Macías, P Morales-Álvarez, Y Wu… - Artificial Intelligence, 2024 - Elsevier
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 …
被引用次数：1 相关文章

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

FM Castro-Macías, P Morales-Álvarez… - Artificial …, 2024 - scholars.northwestern.edu
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 …

[引用][C] Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

FM Castro-Macías, P Morales-Álvarez, Y Wu, R Molina… - 2024 - philpapers.org
Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu, Rafael Molina & Aggelos K.
Katsaggelos, Hyperbolic Secant representation of the logistic function: Application to …
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[PDF][PDF] Hyperbolic Secant Representation of the Logistic Function: Application to Probabilistic Multiple Instance Learning for CT Intracranial Hemorrhage Detection

FM Castro-Macıasa, P Morales-Alvarezc, Y Wud… - ccia.ugr.es
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 …

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

FM Castro-Macías, P Morales-Álvarez, Y Wu… - arXiv e …, 2024 - ui.adsabs.harvard.edu
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 …

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

FM Castro-Macías, P Morales-Álvarez, Y Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …