# Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

arXiv: https://arxiv.org/abs/2301.04423 · HTML: https://mlsanity.com/paper/2301.04423/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset](https://mlsanity.com/paper/2201.11446/) (2022)
- [Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge](https://mlsanity.com/paper/2108.11269/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images](https://mlsanity.com/paper/2301.09431/) (2023)
- [Rethinking U-net Skip Connections for Biomedical Image Segmentation](https://mlsanity.com/paper/2402.08276/) (2024)
- [SCORPION: Addressing Scanner-Induced Variability in Histopathology](https://mlsanity.com/paper/2507.20907/) (2025)
- [FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification](https://mlsanity.com/paper/2511.12044/) (2025)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)

## Abstract (arXiv)

In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.