# A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images

arXiv: https://arxiv.org/abs/2104.12478 · HTML: https://mlsanity.com/paper/2104.12478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks](https://mlsanity.com/paper/1901.11489/) (2019)
- [Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning](https://mlsanity.com/paper/1511.05286/) (2015)
- [Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images](https://mlsanity.com/paper/1703.01550/) (2017)
- [Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learning](https://mlsanity.com/paper/2102.05543/) (2021)

## Abstract (arXiv)

Gastric diffuse-type adenocarcinoma represents a disproportionately high percentage of cases of gastric cancers occurring in the young, and its relative incidence seems to be on the rise. Usually it affects the body of the stomach, and presents shorter duration and worse prognosis compared with the differentiated (intestinal) type adenocarcinoma. The main difficulty encountered in the differential diagnosis of gastric adenocarcinomas occurs with the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are often single and inconspicuous in a background desmoplaia and inflammation, it can often be mistaken for a wide variety of non-neoplastic lesions including gastritis or reactive endothelial cells seen in granulation tissue. In this study we trained deep learning models to classify gastric diffuse-type adenocarcinoma from WSIs. We evaluated the models on five test sets obtained from distinct sources, achieving receiver operator curve (ROC) area under the curves (AUCs) in the range of 0.95-0.99. The highly promising results demonstrate the potential of AI-based computational pathology for aiding pathologists in their diagnostic workflow system.