# Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction

arXiv: https://arxiv.org/abs/2205.06672 · HTML: https://mlsanity.com/paper/2205.06672/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides](https://mlsanity.com/paper/2101.07903/) (2021)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)

## Built on by (most cited first, 14 of 14)

- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [An Aggregation of Aggregation Methods in Computational Pathology](https://mlsanity.com/paper/2211.01256/) (2022)
- [HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis](https://mlsanity.com/paper/2309.07400/) (2023)
- [Realistic Data Enrichment for Robust Image Segmentation in Histopathology](https://mlsanity.com/paper/2304.09534/) (2023)
- [Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study](https://mlsanity.com/paper/2301.09617/) (2023)
- [M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis](https://mlsanity.com/paper/2407.17267/) (2024)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Unsupervised Latent Stain Adaptation for Computational Pathology](https://mlsanity.com/paper/2406.19081/) (2024)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classification](https://mlsanity.com/paper/2211.07384/) (2022)
- [BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology](https://mlsanity.com/paper/2503.20880/) (2025)
- [Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning](https://mlsanity.com/paper/2404.01446/) (2024)
- [CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images](https://mlsanity.com/paper/2408.00427/) (2024)
- [Graph Conditioned Diffusion for Controllable Histopathology Image Generation](https://mlsanity.com/paper/2510.07129/) (2025)

## Abstract (arXiv)

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.