# Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment

arXiv: https://arxiv.org/abs/2202.08510 · HTML: https://mlsanity.com/paper/2202.08510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification](https://mlsanity.com/paper/2202.07570/) (2022)

## Abstract (arXiv)

Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artificial intelligence (AI) has brought a great promise to assist pathologist to screen digitalized whole slide images, existing AI systems are limited in fine-grained cancer subclassifications and have little usability in planning cancer treatment. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance. The AI system is designed to efficiently differentiate multi-classes of GC through multi-scale self-attention mechanism using 2-stage hybrid Vision Transformer (ViT) networks, by mimicking the way how human pathologists understand histology. The AI system demonstrates reliable diagnostic performance by achieving class-average sensitivity of above 0.85 on a total of 1,212 slides from multicentric cohort. Furthermore, AI-assisted pathologists show significantly improved diagnostic sensitivity by 12% in addition to 18% reduced screening time compared to human pathologists. Our results demonstrate that AI-assisted gastric endoscopic screening has a great potential for providing presumptive pathologic opinion and appropriate cancer treatment of gastric cancer in practical clinical settings.