UCF-MultiOrgan-Path: A Public Benchmark Dataset of Histopathologic Images for Deep Learning Model Based Organ Classification
Hossain, M. S. B.; Piazza, Y.; Braun, J.; Bilic, A.; Hsieh, M.; Fouissi, S.; Borowsky, A.; Kaseb, H.; Fraser, A.; Wray, B.-A.; Chen, C.; Wang, L.; Husain, M.; Hadley, D.
Show abstract
A pathologist typically diagnoses tissue samples by examining glass slides under a light microscope. The entire tissue specimen can be stored digitally as a Whole Slide Image (WSI) for further analysis. However, managing and diagnosing large numbers of images manually is time-consuming and requires specialized expertise. Consequently, computer-aided diagnosis of these pathology images is an active research area, with deep learning showing promise in disease classification and cancer cell segmentation. Robust deep learning models need many annotated images, but public datasets are limited, often constrained to specific organs, cancer types, or binary classifications, which limits generalizability. To address this, we introduce the UCF multi-organ histopathologic (UCF-MultiOrgan-Path) dataset, containing 977 WSIs from cadaver tissues across 15 organ classes, including lung, kidney, liver, and pancreas. This dataset includes [~]2.38 million patches of 512x512 pixels. For technical validation, we provide patch-based and slide-based approaches for patch- and slide-level classification. Our dataset, containing millions of patches, can serve as a benchmark for training and validating deep learning models in multi-organ classification.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis 97%
- ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning 96%
- Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation 95%
Similar papers in this journal
- ViFIT-assisted Histopathology: From H&E Style Standardization to Virtual Fiber Image Transformation 95%
- Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning 95%
- A Framework for Falsifiable Explanations of Machine Learning Models with an Application in Computational Pathology 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.