Ruben-F/bloodcelldiff

This model is a YOLOv8-based object detection model specifically trained for detecting and classifying blood cells in microscopy images. It aims to identify various types of blood cells, such as Red Blood Cells (RBC), Platelets (PLT), and several types of White Blood Cells (WBC), including Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils.

Model Details

Model Description

This model is built upon the YOLOv8 architecture and trained to detect and classify different types of blood cells from microscopy images. The model has been fine-tuned to recognize specific cell types, making it suitable for applications in medical diagnostics and research.

• Developed by:
• Model type: YOLOv8 (Object Detection)
• License: [Specify License, e.g., MIT]
• Finetuned from model: YOLOv8s

Model Sources [optional]

• Repository: https://huggingface.co/Ruben-F/bloodcelldiff

Uses

Direct Use

This model can be used directly for detecting and classifying blood cells in microscopy images. It can be integrated into medical imaging pipelines or used for annotating blood cell images.

Downstream Use [optional]

This model can be fine-tuned for other related tasks or integrated into broader biomedical imaging systems where precise blood cell detection is required.

Out-of-Scope Use

This model is not suitable for general object detection tasks outside of blood cell classification or for use with non-microscopy images where blood cells are not present.

Bias, Risks, and Limitations

Bias

The model is trained on specific blood cell types and may not generalize well to other types of cells or variations in microscopy image quality. Ensure that the dataset used for training is representative of the scenarios where the model will be applied.

Risks

Misclassifications may occur if the model encounters unfamiliar cell types or poorly captured images. Always validate the model's predictions with domain experts.

Limitations

The model's performance is contingent on the quality and diversity of the training dataset. It may not perform optimally in cases of extreme image noise or unusual cell appearances.

Recommendations

Users should be aware of the model's limitations and verify predictions in critical applications. Continuous monitoring and periodic re-evaluation with updated datasets are recommended.