# Multi-Stage Pathological Image Classification using Semantic Segmentation

arXiv: https://arxiv.org/abs/1910.04473 · HTML: https://mlsanity.com/paper/1910.04473/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [Gigapixel Whole-Slide Images Classification using Locally Supervised Learning](https://mlsanity.com/paper/2207.08267/) (2022)
- [Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning](https://mlsanity.com/paper/2303.12214/) (2023)
- [Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model](https://mlsanity.com/paper/2304.03572/) (2023)
- [Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation](https://mlsanity.com/paper/2101.09321/) (2021)
- [Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation](https://mlsanity.com/paper/2203.08667/) (2022)
- [Hybrid Supervision Learning for Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2107.00934/) (2021)
- [Exploring Generalizable Distillation for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.12995/) (2022)
- [FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration](https://mlsanity.com/paper/2305.05222/) (2023)
- [Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation](https://mlsanity.com/paper/2511.05034/) (2025)
- [Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images](https://mlsanity.com/paper/2304.03537/) (2023)
- [Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance](https://mlsanity.com/paper/2305.05228/) (2023)
- [Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides](https://mlsanity.com/paper/2304.09314/) (2023)

## Abstract (arXiv)

Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel resolution considering the available memory capacity. Most of the previous works divide high resolution WSIs into small image patches and separately input them into the model to classify it as a tumor or a normal tissue. However, patch-based classification uses only patch-scale local information but ignores the relationship between neighboring patches. If we consider the relationship of neighboring patches and global features, we can improve the classification performance. In this paper, we propose a new model structure combining the patch-based classification model and whole slide-scale segmentation model in order to improve the prediction performance of automatic pathological diagnosis. We extract patch features from the classification model and input them into the segmentation model to obtain a whole slide tumor probability heatmap. The classification model considers patch-scale local features, and the segmentation model can take global information into account. We also propose a new optimization method that retains gradient information and trains the model partially for end-to-end learning with limited GPU memory capacity. We apply our method to the tumor/normal prediction on WSIs and the classification performance is improved compared with the conventional patch-based method.