# DiagSet: a dataset for prostate cancer histopathological image classification

arXiv: https://arxiv.org/abs/2105.04014 · HTML: https://mlsanity.com/paper/2105.04014/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer](https://mlsanity.com/paper/1811.06497/) (2018)
- [Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images](https://mlsanity.com/paper/1806.01357/) (2018)
- [Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise](https://mlsanity.com/paper/2004.03406/) (2020)
- [Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology](https://mlsanity.com/paper/1805.06983/) (2018)
- [Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders](https://mlsanity.com/paper/1804.07098/) (2018)

## Built on by (most cited first, 13 of 13)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [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)
- [The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population](https://mlsanity.com/paper/2512.03854/) (2025)
- [MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification](https://mlsanity.com/paper/2509.00311/) (2025)
- [Whole Slide Concepts: A Supervised Foundation Model For Pathological Images](https://mlsanity.com/paper/2507.05742/) (2025)
- [A Unified Low-level Foundation Model for Enhancing Pathology Image Quality](https://mlsanity.com/paper/2509.01071/) (2025)
- [A Deployment-Friendly Foundational Framework for Efficient Computational Pathology](https://mlsanity.com/paper/2602.14010/) (2026)
- [Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation](https://mlsanity.com/paper/2111.04893/) (2021)
- [Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2210.11177/) (2022)
- [Graph Laplacian Transformer with Progressive Sampling for Prostate Cancer Grading](https://mlsanity.com/paper/2512.10808/) (2025)
- [PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning](https://mlsanity.com/paper/2604.12100/) (2026)
- [CoM3eT: A foundation model for medical image analysis through federated, multidimensional context integration](https://mlsanity.com/paper/2608.16268/) (2026)

## Abstract (arXiv)

Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses, and 46 scans with diagnoses independently provided by a group of histopathologists can be found at https://github.com/michalkoziarski/DiagSet. Furthermore, we propose a machine learning framework for detection of cancerous tissue regions and prediction of scan-level diagnosis, utilizing thresholding to abstain from the decision in uncertain cases. The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition and is compared in a scan-level diagnosis with 9 human histopathologists showing high statistical agreement.