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README.md

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 ---
-license: mit
-tags:
-- biology
-- pathology
-- TCGA
-- Prostate
-pretty_name: Refined TCGA-PRAD Prostate Cancer Pathology Dataset
-size_categories:
-- 1K<n<10K
+title: README
+emoji: 👁
+colorFrom: indigo
+colorTo: gray
+sdk: static
+pinned: false
 ---
 
-Dataset Description: A Second Opinion on TCGA PRAD Prostate Dataset Labels Supported by ROI-Level Annotations 
-Motivation:
-We discovered that some diagnostic labels in the TCGA's PRAD prostate cancer dataset were incorrect, and some are incomplete in terms of describing the Gleason patterns presented on the slides. This presents a significant issue, as many recent AI pathology studies show models achieving higher accuracy on the TCGA dataset. However, if the labels are wrong, a higher accuracy on incorrect labels could suggest that the model is performing worse than it actually is when compared to true labels. To address this, Codatta collaborated with DPath and expert-level pathologists to re-annotate some of the TCGA PRAD dataset, improving the label quality and ensuring more reliable data for AI model training.
+
+# Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations
+
+## Overview
+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.
+
+## Motivation
+We discovered significant inconsistencies in the diagnostic labels of the TCGA PRAD dataset, where:
+* Some labels could be enhanced with additional opinons (labels).
+* Some labels lacked granular descriptions of the Gleason patterns.
+
+This presents a challenge for AI pathology models, as reported high accuracy might reflect learning from improved labels. To address this:
+* Pathologists re-annotated slides to improve label quality.
+* ROI annotations were introduced to clearly differentiate Gleason tumor grades.
+* Each annotation is supported by detailed reasoning, providing transparency and justification for corrections.
+
+## Dataset Contents
+This dataset includes two primary files:
+1. Slide-Level Labels ([PRAD.csv](https://huggingface.co/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset/blob/main/dataset/PRAD/PRAD.csv))
+   * Contains comprehensive metadata and diagnostic details:
+      * `slide_id`: Unique slide identifier.
+      * `slide_name`: TCGA Whole Slide Image (WSI) name.
+      * `label`: Corrected Gleason grade (e.g., 4+3, 5+4).
+      * `diagnosis`: Pathologist-provided reasoning for the label.
+      * `num_rois`: Number of labeled Regions of Interest (ROIs) per slide.
+
+2. ROI-Level Annotations (**.geojson**)
+    * Provides spatial coordinates for tumor regions:
+      * Each ROI corresponds to specific Gleason grades (e.g., Grade 3, Grade 4).
+      * Compatible with tools like QuPath for interactive visualization.
+
+## Key Statistics
 | Type                     | Counts |
 |--------------------------|--------|
 | TCGA WSI Total           | 435    |
 | Agree with TCGA label    | 190    |
 | Disagree with TCGA label | 245    |
+> We are currently in the process of uploading data files to meet the quantities mentioned above. This is an ongoing effort to balance community impact and data quality.
+
+
+## Re-Annotation Process
+1. **Curation of Cases for Enhancement**: An expert committee identified slides requiring review.
+2. **Annotation**: Junior pathologists performed initial ROI-level annotations.
+3. **Expert Review**: Senior pathologists validated and refined the annotations.
+4. **Enhancements**:
+    * Granular ROI labeling for tumor regions.
+    * GIntroduction of Minor Grades: For example, Minor Grade 5 indicates <5% of Gleason Grade 5 tumor presence.
+    * GPathologist Reasoning: Each label includes a detailed explanation of the annotation process.
+Some labels can be improved by adding alternative opinions to enhance the labels further.
+
+## Improvements Over TCGA Labels
+* **Accuracy**: Enhanced slide-level Gleason labels with additional opinions and improved granularity.
+* **Granularity**: Clear ROI-level annotations for primary, secondary, and minor tumor grades.
+* **Transparency**: Pathologist-provided reasoning ensures a chain of thought for label decisions.
+
+**Example**:
+* Original TCGA label: Gleason Grade 4+3.
+* Enhanced label: Gleason Grade 4+3 + Minor Grade 5.
+
+## Usage
+### For AI Training Pipelines
+Combine Whole Slide Images (WSI) from TCGA PRAD with this dataset's slide-level labels (PRAD.csv) and ROI annotations (.geojson) to generate high-quality [X, y] pairs.
+### For Pathology Research
+Use the ROI annotations in WSI viewers (e.g., QuPath) to interactively visualize labeled tumor regions.
+Explore detailed reasoning behind Gleason grade decisions to understand tumor composition.
+### How to Load the Dataset
+1. **CSV File**
+Use pandas to explore slide-level metadata:
+```python
+import pandas as pd
+df = pd.read_csv("PRAD.csv")
+print(df.head())
+```
+
+2. **GeoJSON File**
+Load ROIs using tools like QuPath or GeoPandas:
+```python
+import geopandas as gpd
+roi_data = gpd.read_file("annotations.geojson")
+roi_data.plot()
+```
+
+3. TCGA Whole Slide Images
+Original WSI files can be downloaded from the GDC Portal.
+Match WSI filenames with the `slide_name` column in `PRAD.csv` for integration.
+
+## Example Workflow
+Download **[TCGA PRAD](https://portal.gdc.cancer.gov/projects/TCGA-PRAD)** slides from the GDC Portal.
+**Load** [`PRAD.csv`](https://huggingface.co/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset/blob/main/dataset/PRAD/PRAD.csv) to access corrected labels and reasoning.
+Visualize ROI annotations using the .geojson files.
+Train AI models with X = WSI images, y = ROI annotations + slide-level labels.
+
+## Licensing
+This dataset is provided under a **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
 
-Re-Annotation Process:
-To support our new slide-level labels, pathologists meticulously labeled the corresponding regions of interest (ROIs) within each slide. For example, when a slide was labeled as Gleason Grade 4+3, the regions containing Gleason Grade 4 tumor and Gleason Grade 3 tumor were separately marked. This provided a clear indication of the relative proportion of these tumor grades within the slide, helping clarify the grading structure. Each label also includes a paragraph of reasoning, offering a chain of thought that explains how the pathologist arrived at the label, ensuring transparency and justification for the second-opinion annotations.
+## Credits
+This dataset is a collaboration between:
+* [Codatta](https://codatta.io) and the network of anynmous pathologists
+* [DPath.ai](https://dpath.ai)
+Special thanks to the Hugging Face community for hosting this dataset.
 
-Improvements Over TCGA Labels:
-In addition to correcting TCGA's slide-level labels, we made the grading system more detailed. The TCGA labels only distinguish between primary and secondary grades for prostate cancer. Our annotations introduce a "Minor" category, which provides a more nuanced understanding of the tumor composition. For example, a slide labeled as Gleason Grade 4+3 might also be annotated with Minor Grade 5, indicating that less than 5% of the slide contains Grade 5 tumor, which is valuable for prognosis and treatment planning.
+## Contact
+For questions, suggestions, or collaborations (launch custom data sourcing and labeling tasks), please reach out via:
+* **Email**: hello@codatta.io
+* **Website**: https://codatta.ai
 
-How to Use:
-This dataset includes two key files to facilitate its use: (1) an Excel file containing comprehensive slide-level information, including the slide name, diagnosis, label, and the number of regions of interest (ROIs) for each slide, and (2) a JSON file that provides the ROI coordinates for each slide. The Excel file serves as a detailed reference for understanding the annotations and slide-level statistics. The JSON file can be directly opened with QuPath or other whole slide image (WSI) viewers, allowing users to visualize the ROIs interactively on the slides. This setup makes it easy to explore the data, validate the annotations, and integrate the dataset into AI training pipelines or pathology workflows.
+## Citing This Work
+If you use this dataset in your research or application, please cite it as:
+```
+@dataset{codatta_tcga_prad_roi,
+  title={A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations},
+  author={codatta, DPath, and Expert Pathologists},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/codatta/TCGA-PRAD-annotations}
+}
+```