ArticleScientific reports2024
DiagSet: a dataset for prostate cancer histopathological image classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 39 citations in OpenAlex.
- The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.Scientific data · 2026Article
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- PANDA-PLUS: Improved dataset of prostate whole slide images from PANDA Challenge with pixel-level expert annotations.Journal of pathology informatics · 2026Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.ArXiv · 2025Article
- Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.Scientific reports · 2025Article
- Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody-Associated Glomerulonephritis.Kidney international reports · 2025Article
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.Advances in neural information processing systems · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Data-driven color augmentation for H&E stained images in computational pathology.Journal of pathology informatics · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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