# Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

arXiv: https://arxiv.org/abs/2201.11446 · HTML: https://mlsanity.com/paper/2201.11446/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control](https://mlsanity.com/paper/2004.14595/) (2020)
- [Quantifying the Scanner-Induced Domain Gap in Mitosis Detection](https://mlsanity.com/paper/2103.16515/) (2021)
- [Improving Whole Slide Segmentation Through Visual Context - A Systematic Study](https://mlsanity.com/paper/1806.04259/) (2018)

## Built on by (most cited first, 17 of 17)

- [WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images](https://mlsanity.com/paper/2403.09257/) (2024)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology](https://mlsanity.com/paper/2211.16141/) (2022)
- [Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset](https://mlsanity.com/paper/2301.04423/) (2023)
- [Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation](https://mlsanity.com/paper/2307.07168/) (2023)
- [Rethinking U-net Skip Connections for Biomedical Image Segmentation](https://mlsanity.com/paper/2402.08276/) (2024)
- [Re-identification from histopathology images](https://mlsanity.com/paper/2403.12816/) (2024)
- [Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2403.14429/) (2024)
- [PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting](https://mlsanity.com/paper/2503.04634/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols](https://mlsanity.com/paper/2608.30420/) (2026)
- [Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification](https://mlsanity.com/paper/2409.11110/) (2024)
- [Decomposition Sampling for Efficient Region Annotations in Active Learning](https://mlsanity.com/paper/2512.07606/) (2025)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)
- [VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology](https://mlsanity.com/paper/2608.26382/) (2026)
- [Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images](https://mlsanity.com/paper/2609.31040/) (2026)
- [Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology](https://mlsanity.com/paper/2610.17233/) (2026)

## Abstract (arXiv)

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.