# UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

arXiv: https://arxiv.org/abs/2101.09991 · HTML: https://mlsanity.com/paper/2101.09991/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images](https://mlsanity.com/paper/1703.01550/) (2017)
- [Evolutionary Neural Architecture Search for Image Restoration](https://mlsanity.com/paper/1812.05866/) (2018)

## Built on by (most cited first, 30 of 33)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings](https://mlsanity.com/paper/2511.23204/) (2025)
- [H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression](https://mlsanity.com/paper/2201.06329/) (2022)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful](https://mlsanity.com/paper/2409.11546/) (2024)
- [Prompt-Guided Foundation Model Tuning for Pathology Image Classification](https://mlsanity.com/paper/2403.12537/) (2024)
- [CAMP: Continuous and Adaptive Learning Model in Pathology](https://mlsanity.com/paper/2407.09030/) (2024)
- [Dataset Distillation for Histopathology Image Classification](https://mlsanity.com/paper/2408.09709/) (2024)
- [Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections](https://mlsanity.com/paper/2411.10752/) (2024)
- [Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology](https://mlsanity.com/paper/2503.18709/) (2025)
- [Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning](https://mlsanity.com/paper/2505.15687/) (2025)
- [AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image](https://mlsanity.com/paper/2303.06371/) (2023)
- [Multi-target stain normalization for histology slides](https://mlsanity.com/paper/2406.02077/) (2024)
- [Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint](https://mlsanity.com/paper/2404.00351/) (2024)
- [Dysplasia grading of colorectal polyps through CNN analysis of WSI](https://mlsanity.com/paper/2102.05498/) (2021)
- [Unifying Multiple Foundation Models for Advanced Computational Pathology](https://mlsanity.com/paper/2503.00736/) (2025)
- [Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology](https://mlsanity.com/paper/2506.19234/) (2025)
- [Whole Slide Concepts: A Supervised Foundation Model For Pathological Images](https://mlsanity.com/paper/2507.05742/) (2025)
- [MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models](https://mlsanity.com/paper/2508.00383/) (2025)
- [A Generative Foundation Model for Multimodal Histopathology](https://mlsanity.com/paper/2604.03635/) (2026)
- [Lung nodules segmentation from CT with DeepHealth toolkit](https://mlsanity.com/paper/2208.00641/) (2022)
- [UniToBrain dataset: a Brain Perfusion Dataset](https://mlsanity.com/paper/2208.00650/) (2022)
- [Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification](https://mlsanity.com/paper/2401.14206/) (2024)
- [Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing](https://mlsanity.com/paper/2502.00594/) (2025)

## Abstract (arXiv)

Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. We present our dataset and provide insights on how to tackle the problem of automatic colorectal polyps characterization.