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  # Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations
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  ## Overview
 
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  This dataset provides enhanced and corrected Gleason grading annotations for the TCGA PRAD prostate cancer dataset, supported by Region of Interest (ROI)-level spatial annotations. Developed in collaboration with **[Codatta](https://codatta.io)** and **[DPath.ai](https://dpath.ai)**, where **[DPath.ai](https://dpath.ai)** launched a dedicated community via **[Codatta](https://codatta.io)** to assemble a network of pathologists, this dataset improved accuracies and granularity of information in the original **[TCGA-PRAD](https://portal.gdc.cancer.gov/projects/TCGA-PRAD)** slide-level labels. The collaborative effort enabled pathologists worldwide to contribute annotations, improving label reliability for AI model training and advancing pathology research. Unlike traditional labeling marketplaces, collaborators a.k.a pathologists retain ownership of the dataset, ensuring their contributions remain recognized, potentially rewarded and valuable within the community. Please cite the dataset in any publication or work using the provided citation format to acknowledge the collaborative efforts of **[Codatta](https://codatta.io)**, **[DPath.ai](https://dpath.ai)**, and the contributing pathologists.
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  ## Motivation
 
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  # Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations
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  ## Overview
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+ ![Exmaple of Annotated WSI](cover_picture.png)
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  This dataset provides enhanced and corrected Gleason grading annotations for the TCGA PRAD prostate cancer dataset, supported by Region of Interest (ROI)-level spatial annotations. Developed in collaboration with **[Codatta](https://codatta.io)** and **[DPath.ai](https://dpath.ai)**, where **[DPath.ai](https://dpath.ai)** launched a dedicated community via **[Codatta](https://codatta.io)** to assemble a network of pathologists, this dataset improved accuracies and granularity of information in the original **[TCGA-PRAD](https://portal.gdc.cancer.gov/projects/TCGA-PRAD)** slide-level labels. The collaborative effort enabled pathologists worldwide to contribute annotations, improving label reliability for AI model training and advancing pathology research. Unlike traditional labeling marketplaces, collaborators a.k.a pathologists retain ownership of the dataset, ensuring their contributions remain recognized, potentially rewarded and valuable within the community. Please cite the dataset in any publication or work using the provided citation format to acknowledge the collaborative efforts of **[Codatta](https://codatta.io)**, **[DPath.ai](https://dpath.ai)**, and the contributing pathologists.
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  ## Motivation