  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.
