Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification

• URL: http://arxiv.org/abs/2307.07482v2
• Date: Fri, 17 Nov 2023 11:30:33 GMT
• Title: Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
• Authors: Simon Holdenried-Krafft and Peter Somers and Ivonne A. Montes-Majarro and Diana Silimon and Cristina Tar\'in and Falko Fend and Hendrik P. A. Lensch
• Abstract summary: Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. Coarse-grained labels are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL) We propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL)
• Score: 5.121989578393729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysis. Precise annotations for patch- or even pixel-level classifications in the context of gigapixel WSIs are tedious to acquire and require domain experts. Coarse-grained labels, on the other hand, are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL). In our work, we propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL). We contribute to both the embedding and aggregation steps. Since all-purpose visual feature representations are not yet available, embedding models are currently limited in terms of generalizability. With our work, we explore the potential of dynamic meta-embedding based on cutting-edge self-supervised pre-trained models in the context of MIL. Moreover, we propose a new MIL architecture capable of combining MIL-attention with correlated self-attention. The Dual-Query Perceiver design of our approach allows us to leverage the concept of self-distillation and to combine the advantages of a small model in the context of a low data regime with the rich feature representation of a larger model. We demonstrate the superior performance of our approach on three histopathological datasets, where we show improvement of up to 10% over state-of-the-art approaches.

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