Mitosis detection: Datasets & Models
Automated detection of mitotic figures in histopathology images using object detection. Identifies dividing cells across whole-slide scans and tissue microscopy.
Mitosis detection
This detection workflow performs mitosis detection on stained histopathology images, drawing bounding boxes around each mitotic figure and separating true dividing cells from lookalikes such as apoptotic bodies and dense nuclei. It accepts microscopy images or tiled regions cropped from whole-slide scans as input and returns the location and count of mitoses to support cancer grading.
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Why automate mitosis detection
Manual mitosis counting requires a pathologist to scan dozens of high-power fields under a microscope, a process that is slow, fatiguing, and prone to inter-observer variability that directly affects tumor grading. Automating mitotic figure detection with a vision model gives consistent, reproducible results and lets specialists focus review time on ambiguous cases rather than exhaustive counting. In healthcare imaging pipelines, this means faster turnaround on grading and a defensible, quantitative record of where each detection occurred on the slide.
Key benefits
An RF-DETR detection model can flag candidate mitotic figures across an entire slide and rank fields by mitotic density, cutting the manual search space substantially. It integrates with existing whole-slide scanners and digital pathology viewers, and scales from single-slide review to batch processing of large case backlogs without adding headcount.
How to use it
- Upload an image: Drop any photo into the widget above to try mitosis detection for free, right on this page.
- Preview instant results: The model detects mitotic figure, dividing cell, cell nucleus, apoptotic body, tumor cell within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for mitosis detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for mitosis detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
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