# Deep Features for Tissue-Fold Detection in Histopathology Images

arXiv: https://arxiv.org/abs/1903.07011 · HTML: https://mlsanity.com/paper/1903.07011/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs](https://mlsanity.com/paper/2403.07743/) (2024)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation](https://mlsanity.com/paper/2406.11538/) (2024)
- [NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies](https://mlsanity.com/paper/2311.15113/) (2023)

## Abstract (arXiv)

Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against the normal tissue in hematoxylin and eosin (H&E) stained biopsy samples. In our experiments, we manually select the folded area in roughly 2.5mm × 2.5mm patches at 20x magnification level as the training data. The ``DenseNet'' with 201 layers alongside an SVM classifier outperformed all other configurations. Based on the leave-one-out validation strategy, we achieved 96.3% accuracy, whereas with augmentation the accuracy increased to 97.2%. We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was 81%. One folded patch within an image suffices to flag the entire specimen for visual inspection.