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PTCMIL: multiple instance learning via prompt token clustering for whole slide image analysis.

Beidi Zhao¹, Sangmook Kim², Hao Chen³

¹University of British Columbia, 2332 Main Mall, Vancouver, V6T 1Z4, BC, Canada; Vector Institute, 108 College St W1140, M5G 0C6, ON, Toronto, Canada.

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Summary

This summary is machine-generated.

This study introduces PTCMIL, a new method for analyzing whole slide images (WSIs) using prompt token clustering within Vision Transformers. PTCMIL effectively aggregates diverse patch information for improved cancer classification and survival prediction in computational pathology.

Keywords:

Clustering Multiple instance learning Prompt learning

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Area of Science:

Computational pathology
Artificial intelligence in medicine
Digital pathology

Background:

Multiple Instance Learning (MIL) is crucial for whole slide image (WSI) analysis but struggles with WSI complexity and heterogeneity.
Existing MIL methods often fail to aggregate diverse patch information effectively, leading to suboptimal WSI representations.
Vision Transformers (ViTs) and clustering show promise but are computationally intensive and may not capture task-specific features or slide variability.

Purpose of the Study:

To develop a novel method, PTCMIL (Prompt Token Clustering-based ViT), for improved MIL aggregation in WSIs.
To address the limitations of existing MIL approaches in capturing slide-specific variability and task-aware features.
To enhance the robustness and predictive power of WSI representations for various computational pathology tasks.

Main Methods:

PTCMIL integrates learnable prompt tokens into a Vision Transformer (ViT) backbone for slide-specific, task-aware clustering.
A projection-based token clustering mechanism guides clustering with prediction objectives.
A prompt-driven clustering approach with an efficient token merging strategy generates compact WSI-level representations.

Main Results:

PTCMIL consistently outperformed state-of-the-art MIL baselines across eleven benchmark datasets, including various cancer types.
The method demonstrated superior performance in classification, survival prediction, and domain adaptation tasks.
PTCMIL effectively captures patch diversity and task-relevant patterns, generating interpretable and compact WSI representations.

Conclusions:

PTCMIL offers a practical and generalizable solution for large-scale computational pathology.
The prompt-driven clustering and efficient merging strategy provide an effective way to aggregate WSI information.
The proposed pooling module supports diverse tasks, including classification and survival analysis, highlighting PTCMIL's versatility.