dwb2023/roboflow100-bccd-florence2

Dataset Card for roboflow-bccd-florrence2

Dataset Summary

This dataset, roboflow-bccd-paligemma, is a modified version of the BCCD (Blood Cell Count and Detection) dataset. It contains blood cell images annotated for object detection tasks, specifically targeting three types of blood cells:

1. Platelets
2. Red Blood Cells (RBC)
3. White Blood Cells (WBC)

Key features of the dataset:

• Total of 364 annotated images across train, validation, and test splits
• Bounding box annotations for each detected cell
• Labels identifying the cell types

The dataset is structured to support object detection tasks in the medical imaging domain, particularly for blood cell analysis.

It's crucial to note that this dataset is a derivative work based on the original BCCD dataset. When using this dataset, proper attribution is essential. Please use the citation provided at the end of this card in any work that utilizes this data.

Supported Tasks and Leaderboards

• Tasks: Object Detection

Languages

The dataset uses English labels.

Dataset Structure

Data Instances

A typical data instance contains:

• An image of blood cells
• Bounding box annotations for detected cells
• Labels identifying the cell types (Platelets, RBC, WBC)

Data Fields

• image_id: Unique identifier for each image
• image: The blood cell image
• annotations: Contains annotation details

The annotations field has the following structure:

Field Name	Description	Data Type
image	Identifier for the image being annotated	string
prefix	Standard prefix for all annotations, typically "detect Platelets ; RBC ; WBC"	string
suffix	Contains the actual annotation data, including bounding box coordinates and cell type labels	string

The suffix field contains multiple annotations for each image, separated by semicolons. Each annotation typically follows this format:

CellType<loc_x1><loc_y1><loc_x2><loc_y2>

Where:

• <loc_x1><loc_y1><loc_x2><loc_y2> represent the bounding box coordinates
• CellType is one of: RBC, WBC, or Platelets

Example of a complete annotation:

{
"image": "BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg",
"prefix": "<OD>",
"suffix": "RBC<loc_756><loc_406><loc_958><loc_631>RBC<loc_820><loc_623><loc_995><loc_825>RBC<loc_150><loc_327><loc_355><loc_508>RBC<loc_283><loc_685><loc_283><loc_685>RBC<loc_400><loc_417><loc_588><loc_640>RBC<loc_817><loc_2><loc_999><loc_248>RBC<loc_50><loc_10><loc_208><loc_204>RBC<loc_206><loc_46><loc_314><loc_302>RBC<loc_528><loc_677><loc_670><loc_925>Platelets<loc_2><loc_752><loc_75><loc_854>Platelets<loc_109><loc_429><loc_184><loc_531>WBC<loc_286><loc_2><loc_664><loc_319>"
}

Data Splits

The dataset is divided into three splits:

• Train: 255 images
• Validation: 73 images
• Test: 36 images

Considerations for Using the Data

Social Impact of Dataset

This dataset could potentially aid in automating blood cell counting and classification, which may improve efficiency in medical diagnostics. However, as with any medical-related AI application, care must be taken to ensure accuracy and proper validation before clinical use.

Additional Information

Licensing Information

This dataset is licensed under Apache 2.0.

Citation Information

If you use this dataset in your research, please cite it as:

@misc{
  bccd-ouzjz_dataset,
  title = { bccd Dataset },
  type = { Open Source Dataset },
  author = { Roboflow 100 },
  howpublished = { \url{ https://universe.roboflow.com/roboflow-100/bccd-ouzjz } },
  url = { https://universe.roboflow.com/roboflow-100/bccd-ouzjz },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { may },
  note = { visited on 2024-08-02 },
}