Deep Multiple Instance Learning with Distance-Aware Self-Attention

• URL: http://arxiv.org/abs/2305.10552v2
• Date: Sat, 20 May 2023 12:45:27 GMT
• Title: Deep Multiple Instance Learning with Distance-Aware Self-Attention
• Authors: Georg W\"olflein and Lucie Charlotte Magister and Pietro Li\`o and David J. Harrison and Ognjen Arandjelovi\'c
• Abstract summary: We introduce a novel multiple instance learning (MIL) model with distance-aware self-attention (DAS-MIL) Unlike existing relative position representations for self-attention which are discrete, our approach introduces continuous distance-dependent terms into the computation of the attention weights. We evaluate our model on a custom MNIST-based MIL dataset and on CAMELYON16, a publicly available cancer metastasis detection dataset.
• Score: 9.361964965928063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-resolution images are split into smaller patches, but labels apply to the image as a whole. Recent MIL models are able to capture correspondences between patches by employing self-attention, allowing them to weigh each patch differently based on all other patches in the bag. However, these approaches still do not consider the relative spatial relationships between patches within the larger image, which is especially important in computational pathology. To this end, we introduce a novel MIL model with distance-aware self-attention (DAS-MIL), which explicitly takes into account relative spatial information when modelling the interactions between patches. Unlike existing relative position representations for self-attention which are discrete, our approach introduces continuous distance-dependent terms into the computation of the attention weights, and is the first to apply relative position representations in the context of MIL. We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0.91. On both datasets, our model outperforms existing MIL approaches that employ absolute positional encodings, as well as existing relative position representation schemes applied to MIL. Our code is available at https://anonymous.4open.science/r/das-mil.

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