Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learning (MIL) has emerged as a powerful approach for addressing these challenges, particularly in cancer classification and detection. This survey provides a comprehensive overview of the challenges and methodologies associated with applying MIL to WSI analysis, including attention mechanisms, pseudo-labeling, transformers, pooling functions, and graph neural networks. Additionally, it explores the potential of MIL in discovering cancer cell morphology, constructing interpretable machine learning models, and quantifying cancer grading. By summarizing the current challenges, methodologies, and potential applications of MIL in WSI analysis, this survey aims to inform researchers about the state of the field and inspire future research directions.
Code (0)
등록된 구현이 없습니다.
Tasks
Cancer ClassificationInterpretable Machine LearningMultiple Instance LearningSurveywhole slide imagesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracted from a whole slide image, which could …
Classificationimage-classificationImage ClassificationMultiple Instance Learning+2Whole Slide Image Classification of Salivary Gland Tumours
This work shows promising results using multiple instance learning on salivary gland tumours in classifying cancers on whole slide images. Utilising CTransPath as a patch-level feature extractor and CLAM as a feature agg…
Classificationimage-classificationImage ClassificationMultiple Instance Learning+1Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be…
Multiple Instance LearningMultiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respec…
Multiple Instance LearningMulti-Scale Prototypical Transformer for Whole Slide Image Classification
Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains chal…
Classificationimage-classificationImage ClassificationMultiple Instance Learning