# Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

arXiv: https://arxiv.org/abs/1807.11113 · HTML: https://mlsanity.com/paper/1807.11113/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [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)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing](https://mlsanity.com/paper/2203.16168/) (2022)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [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)
- [Trusted Multi-Scale Classification Framework for Whole Slide Image](https://mlsanity.com/paper/2207.05290/) (2022)
- [FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images](https://mlsanity.com/paper/2506.07652/) (2025)
- [CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound](https://mlsanity.com/paper/2206.08524/) (2022)
- [Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning](https://mlsanity.com/paper/2309.15372/) (2023)
- [Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism](https://mlsanity.com/paper/2407.19821/) (2024)

## Abstract (arXiv)

Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and using low-magnification patches can cause the loss of information. In this paper, we aim to achieve two seemingly conflicting goals for breast cancer segmentation: accurate and fast prediction. We propose a simple yet efficient framework Reinforced Auto-Zoom Net (RAZN) to tackle this task. Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest. Because the zoom-in action is selective, RAZN is robust to unbalanced and noisy ground truth labels and can efficiently reduce overfitting. We evaluate our method on a public breast cancer dataset. RAZN outperforms both single-scale and multi-scale baseline approaches, achieving better accuracy at low inference cost.