Surgical Instrument Annotation & QC
Budget: $15 – $25 AUD
I need to push a first MVP batch of 1000 surgical video frames through quality control in the next 1–2 weeks. The frames already carry draft labels from our automated platform; your job is to open each frame in that tool in the Specialised Surgical registry Platform and correct every error, and make sure every instrument—scissors, forceps, clamps and needle drivers—has both:
• a pixel-accurate semantic segmentation mask
Once this batch is approved, a second stage will follow, where you will annotate these annotated images from scratch with an annotation box in the CVAT platform on the surgical tool's tip.
I will walk you through the workflow, guidelines and naming conventions before you start. What really matters is your speed, accuracy and understanding of how label quality drives model recall, precision and false-positive rates in surgical-tool detection.
Deliverables for Stage 1
• 1000 frames fully verified and corrected using the Surgical Registry platform,
Deliverables for Stage 2
• 1000 frames fully annotated with a detection box and verified against ground truth annotation
When you apply, skip long proposals; simply highlight relevant experience with medical or technical image annotation, any metrics that prove you hit deadlines without sacrificing accuracy, and how you measure annotation accuracy and how this affects AI model training. I’m reviewing candidates as soon as they arrive so we can begin training this week. If this project goes well, this can be scaled up for bigger project
Thank you.
• a pixel-accurate semantic segmentation mask
Once this batch is approved, a second stage will follow, where you will annotate these annotated images from scratch with an annotation box in the CVAT platform on the surgical tool's tip.
I will walk you through the workflow, guidelines and naming conventions before you start. What really matters is your speed, accuracy and understanding of how label quality drives model recall, precision and false-positive rates in surgical-tool detection.
Deliverables for Stage 1
• 1000 frames fully verified and corrected using the Surgical Registry platform,
Deliverables for Stage 2
• 1000 frames fully annotated with a detection box and verified against ground truth annotation
When you apply, skip long proposals; simply highlight relevant experience with medical or technical image annotation, any metrics that prove you hit deadlines without sacrificing accuracy, and how you measure annotation accuracy and how this affects AI model training. I’m reviewing candidates as soon as they arrive so we can begin training this week. If this project goes well, this can be scaled up for bigger project
Thank you.