A Comprehensive Review on Multiple Instance Learning

Fatima, Samman; Ali, Sikandar; Kim, Hee-Cheol

doi:10.3390/electronics12204323

Cited by 15 publications

(2 citation statements)

References 78 publications

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“…Other mentioned methods are extensions or adaptations of conventional ones applicable to single‐instance learning. Comprehensive reviews of the MIL concept and its applications in regular ML tasks can be found in reviews 9–11,38,45–49 …”

Section: Origins Of Multi‐instance Learningmentioning

confidence: 99%

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Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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Section: Origins Of Multi‐instance Learningmentioning

confidence: 99%

“…Comprehensive reviews of the MIL concept and its applications in regular ML tasks can be found in reviews. [9][10][11]38,[45][46][47][48][49]…”

Section: Origins Of Multi-instance Learningmentioning

confidence: 99%

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Zankov,

Madzhidov,

Varnek

et al. 2023

WIREs Comput Mol Sci

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Multi‐Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow‐Band Imaging: A Multicenter Study

Tie,

Li,

Zhu

et al. 2024

The Laryngoscope

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ObjectivesVocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow‐band imaging (NBI) to distinguish benign from malignant VFL.MethodsA total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi‐instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real‐time model performance. A human‐machine comparison involving 100 patients and 12 laryngologists assessed the real‐world effectiveness of the model.ResultsThe model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704–0.946). In the human‐machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved.ConclusionsOur multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings.Level of Evidence3 Laryngoscope, 2024

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