# RCNN for Region of Interest Detection in Whole Slide Images

arXiv: https://arxiv.org/abs/2009.07532 · HTML: https://mlsanity.com/paper/2009.07532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)
- [LanGuSTE: Language-Guided Coarse-to-Fine Patch Selection for Efficient Whole Slide Image Analysis](https://mlsanity.com/paper/2508.14537/) (2025)

## Abstract (arXiv)

Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.