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

arXiv: https://arxiv.org/abs/2010.06440 · HTML: https://mlsanity.com/paper/2010.06440/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2307.04189/) (2023)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection](https://mlsanity.com/paper/2104.14528/) (2021)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer](https://mlsanity.com/paper/2106.02473/) (2021)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification](https://mlsanity.com/paper/2102.10499/) (2021)
- [Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy](https://mlsanity.com/paper/2306.07999/) (2023)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning](https://mlsanity.com/paper/2111.03063/) (2021)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images](https://mlsanity.com/paper/2301.07700/) (2023)
- [cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process](https://mlsanity.com/paper/2407.11448/) (2024)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learning](https://mlsanity.com/paper/2203.07659/) (2022)
- [Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images](https://mlsanity.com/paper/2208.07322/) (2022)
- [BROW: Better featuRes fOr Whole slide image based on self-distillation](https://mlsanity.com/paper/2309.08259/) (2023)
- [DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images](https://mlsanity.com/paper/2407.08503/) (2024)
- [An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification](https://mlsanity.com/paper/2106.02864/) (2021)
- [Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning](https://mlsanity.com/paper/2107.03602/) (2021)
- [Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis](https://mlsanity.com/paper/2206.13115/) (2022)
- [Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification](https://mlsanity.com/paper/2302.09833/) (2023)
- [Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations](https://mlsanity.com/paper/2310.16587/) (2023)
- [NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants](https://mlsanity.com/paper/2301.00815/) (2023)
- [iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer](https://mlsanity.com/paper/2404.01192/) (2024)
- [DCMIL: A Progressive Representation Learning of Whole Slide Images for Cancer Prognosis Analysis](https://mlsanity.com/paper/2510.14403/) (2025)

## Abstract (arXiv)

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.