# Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images

arXiv: https://arxiv.org/abs/2410.03289 · HTML: https://mlsanity.com/paper/2410.03289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training](https://mlsanity.com/paper/2409.19587/) (2024)
- [Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology](https://mlsanity.com/paper/1811.06038/) (2018)
- [Gigapixel Histopathological Image Analysis using Attention-based Neural Networks](https://mlsanity.com/paper/2101.09992/) (2021)
- [HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning](https://mlsanity.com/paper/2408.13818/) (2024)
- [FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology](https://mlsanity.com/paper/2007.06565/) (2020)
- [Ink removal from histopathology whole slide images by combining classification, detection and image generation models](https://mlsanity.com/paper/1905.04385/) (2019)
- [Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images](https://mlsanity.com/paper/2011.15000/) (2020)
- [SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks](https://mlsanity.com/paper/1803.07240/) (2018)
- [Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring](https://mlsanity.com/paper/2303.09930/) (2023)

## Built on by (most cited first, 1 of 1)

- [Destroy Me: Automatic Artifact Generation for Histopathology Images](https://mlsanity.com/paper/2608.27516/) (2026)

## Abstract (arXiv)

We developed a software pipeline for quality control (QC) of histopathology whole slide images (WSIs) that segments various regions, such as blurs of different levels, tissue regions, tissue folds, and pen marks. Given the necessity and increasing availability of GPUs for processing WSIs, the proposed pipeline comprises multiple lightweight deep learning models to strike a balance between accuracy and speed. The pipeline was evaluated in all TCGAs, which is the largest publicly available WSI dataset containing more than 11,000 histopathological images from 28 organs. It was compared to a previous work, which was not based on deep learning, and it showed consistent improvement in segmentation results across organs. To minimize annotation effort for tissue and blur segmentation, annotated images were automatically prepared by mosaicking patches (sub-images) from various WSIs whose labels were identified using a patch classification tool HistoROI. Due to the generality of our trained QC pipeline and its extensive testing the potential impact of this work is broad. It can be used for automated pre-processing any WSI cohort to enhance the accuracy and reliability of large-scale histopathology image analysis for both research and clinical use. We have made the trained models, training scripts, training data, and inference results publicly available at https://github.com/abhijeetptl5/wsisegqc, which should enable the research community to use the pipeline right out of the box or further customize it to new datasets and applications in the future.