Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
This work addresses the challenge of efficiently modeling dependencies in large-scale medical images for cancer biomarker prediction, representing an incremental improvement over existing transformer-based methods by focusing on local regimes.
The paper tackles the problem of predicting multiple genetic alterations from whole slide images by questioning the necessity of global self-attention in transformers and proposes a local attention graph-based transformer for multiple instance learning, achieving state-of-the-art results in mutation prediction for gastrointestinal cancer, such as outperforming existing models on biomarkers like microsatellite instability for colorectal cancer.
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities. On the other hand, Transformers with global self-attention modules have been proposed to model the interdependencies among all instances. However, in this paper we question: Is global relation modeling using self-attention necessary, or can we appropriately restrict self-attention calculations to local regimes in large-scale whole slide images (WSIs)? We propose a general-purpose local attention graph-based Transformer for MIL (LA-MIL), introducing an inductive bias by explicitly contextualizing instances in adaptive local regimes of arbitrary size. Additionally, an efficiently adapted loss function enables our approach to learn expressive WSI embeddings for the joint analysis of multiple biomarkers. We demonstrate that LA-MIL achieves state-of-the-art results in mutation prediction for gastrointestinal cancer, outperforming existing models on important biomarkers such as microsatellite instability for colorectal cancer. Our findings suggest that local self-attention sufficiently models dependencies on par with global modules. Our LA-MIL implementation is available at https://github.com/agentdr1/LA_MIL.