# RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification

Source: [RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification](<https://www.sota2.com/research/paper/68fa687f1e209131cea1c5c6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:44.820Z

Authors: Shujun Wang, Yaxi Zhu, Lequan Yu, Hao Chen, Huangjing Lin, Xiangbo Wan, Xinjuan Fan, Pheng-Ann Hen

Published: 2020-10-13T14:55:47.000Z

## Abstract

The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis. In this paper, we design a recalibrated multi-instance deep learning method (RMDL) to address this challenging problem. We first select the discriminative instances, and then utilize these instances to diagnose diseases based on the proposed RMDL approach. The designed RMDL network is capable of capturing instance-wise dependencies and recalibrating instance features according to the importance coefficient learned from the fused features. Furthermore, we build a large whole-slide gastric histopathology image dataset with detailed pixel-level annotations. Experimental results on the constructed gastric dataset demonstrate the significant improvement on the accuracy of our proposed framework compared with other state-of-the-art multi-instance learning methods. Moreover, our method is general and can be extended to other diagnosis tasks of different cancer types based on WSIs.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Segmentation on Gastric Histopathology Image Dataset Tissue-scale](<https://www.sota2.com/research/sota/image-segmentation-on-gastric-histopathology-image-dataset-tissue-scale.md>): Image Segmentation; Gastric Histopathology Image Dataset Tissue-scale; Accuracy: 86.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.06440>)

[PDF](<https://arxiv.org/pdf/2010.06440.pdf>)