# Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training

arXiv: https://arxiv.org/abs/2409.19587 · HTML: https://mlsanity.com/paper/2409.19587/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Gigapixel Histopathological Image Analysis using Attention-based Neural Networks](https://mlsanity.com/paper/2101.09992/) (2021)
- [Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images](https://mlsanity.com/paper/2011.15000/) (2020)

## Built on by (most cited first, 12 of 12)

- [SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers](https://mlsanity.com/paper/2502.04946/) (2025)
- [Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images](https://mlsanity.com/paper/2410.03289/) (2024)
- [Towards Human-AI Collaboration System for the Detection of Invasive Ductal Carcinoma in Histopathology Images](https://mlsanity.com/paper/2508.07875/) (2025)
- [DiffusionQC: Artifact Detection in Histopathology via Diffusion Model](https://mlsanity.com/paper/2601.12233/) (2026)
- [Reliable Benchmarking of Artifact Detection in Computational Pathology: A Reproducibility and Uncertainty Analysis](https://mlsanity.com/paper/2608.30835/) (2026)
- [Evaluation Metric for Quality Control and Generative Models in Histopathology Images](https://mlsanity.com/paper/2411.01034/) (2024)
- [Efficient Whole Slide Image Classification through Fisher Vector Representation](https://mlsanity.com/paper/2411.08530/) (2024)
- [Classification and Morphological Analysis of DLBCL Subtypes in H&E-Stained Slides](https://mlsanity.com/paper/2411.08531/) (2024)
- [Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images](https://mlsanity.com/paper/2411.08936/) (2024)
- [Scalable Whole Slide Image Representation Using K-Mean Clustering and Fisher Vector Aggregation](https://mlsanity.com/paper/2501.12085/) (2025)
- [Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs](https://mlsanity.com/paper/2602.04046/) (2026)
- [Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images](https://mlsanity.com/paper/2605.16444/) (2026)

## Abstract (arXiv)

Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. For instance, WSIs contain multiple types of tissue regions, at least some of which might not be relevant to the diagnosis. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into six broad tissue regions -- epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. HistoROI is trained using a novel human-in-the-loop and active learning paradigm that ensures variations in training data for labeling-efficient generalization. HistoROI consistently performs well across multiple organs, despite being trained on only a single dataset, demonstrating strong generalization. Further, we have examined the utility of HistoROI in improving the performance of downstream deep learning-based tasks using the CAMELYON breast cancer lymph node and TCGA lung cancer datasets. For the former dataset, the area under the receiver operating characteristic curve (AUC) for metastasis versus normal tissue of a neural network trained using weakly supervised learning increased from 0.88 to 0.92 by filtering the data using HistoROI. Similarly, the AUC increased from 0.88 to 0.93 for the classification between adenocarcinoma and squamous cell carcinoma on the lung cancer dataset. We also found that the performance of the HistoROI improves upon HistoQC for artifact detection on a test dataset of 93 annotated WSIs. The limitations of the proposed model are analyzed, and potential extensions are also discussed.