Details

Autor(en) / Beteiligte

• Waqas, Muhammad
• Ahmed, Syed Umaid
• Tahir, Muhammad Atif
• Wu, Jia
• Qureshi, Rizwan

Titel

Exploring Multiple Instance Learning (MIL): A brief survey

Ist Teil von

• Expert systems with applications, 2024-09, Vol.250, Article 123893

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Multiple Instance Learning (MIL) is a learning paradigm, where training instances are arranged in sets, called bags, and only bag-level labels are available during training. This learning paradigm has been successfully applied in various real-world scenarios, including medical image analysis, object detection, image classification, drug activity prediction, and many others. This survey paper presents a comprehensive analysis of MIL, highlighting its significance, recent advancements, methodologies, applications, and evolving trends across diverse domains. The survey begins by explaining the core principles that form the basis of MIL and how it differs from traditional learning approaches. This sets the foundation for comprehending the distinct challenges and techniques of solving MIL problems. Next, we discuss how supervised learning algorithms are tailored to support MIL and combine this discussion with a review of seminal MIL algorithms as well as the latest innovations that incorporate neural networks, deep learning architectures, and attention techniques. This comprehensive analysis helps to understand the strengths, limitations, and adaptability of these methods across diverse data modalities, complexities, and applications. In summary, this survey paper provides an essential resource for researchers, practitioners, and enthusiasts seeking a comprehensive understanding of Multiple Instance Learning. It covers foundational concepts, traditional methods, recent advancements, and future directions. By providing a holistic view of MIL’s dynamic landscape, this paper aims to inspire further innovation and exploration in this ever-evolving field. •A survey on the current state of the Multiple Instance Learning.•We provide applications of Multiple instance learning in various domains.•We discuss how existing supervised learning algorithms are modified to support MIL.•Publicly available datasets and open research challenges in MIL are discussed.