Back To Projects
Back To Projects

Curation of medical imaging experts for thyroid ultrasound annotation

SUPA used a 12,000+ medical expert network to recruit 50 registered practitioners skilled in medical imaging interpretation.

Problem statement

SUPA’s medical partners require medical specialists to annotate thyroid ultrasound images, many of which have limited available hours per day.

SOLUTION

SUPA leveraged its vast network of medical experts to attract, assess, and onboard qualified medical practitioners, managed through SUPA’s platform.

RESULT

50 qualified experts were recruited within 14 days, with 800 additional practitioners on the waitlist to address surge demand.

Overview

The Problem

As SUPA continues its engagement with leading healthcare providers in Southeast Asia, the Talent Acquisition team was tasked to recruit a team of medical practitioners to annotate thyroid ultrasound images.

The resulting images will be utilized to develop AI models that enhance diagnostic accuracy and improve physician efficiency. A highly skilled team is especially important, given the potential consequences of unreliable diagnosis.

The task required:

  • Access to a wide pool of qualified experts in medical imaging, including radiology
  • The ability to assess and manage a workforce with limited availability, given that most experts have full-time responsibilities 

The Solution

Recognising these requirements, SUPA’s talent acquisition team:

  1. Tapped into an extensive network of 12,000+ healthcare practitioners across Southeast Asia, attracting qualified experts with a strong interest in contributing to medical AI development.
  2. Shortlisted candidates through a series of task-based assessments and video interviews.
  3. Leveraged SUPA’s workforce management platform to design a workflow that guarantees steady annotation output, working around the 2-hours-a-day availability of medical experts.

The Result

Within 14 days, SUPA successfully recruited a team of 50 qualified experts, with an additional 800 experts waitlisted to handle any surge demand from our partners.

These practitioners were also trained on segmentation techniques, allowing them to be swiftly deployed for our Partners’ projects.

Why SUPA?

SUPA’s vast network of domain experts, combined with our experience in managing an on-demand, distributed workforce, puts us in an unmatched position to assemble teams to complete highly complex annotation projects.

Other Projects

Discover the work we do

View All
View All

Data labeling for autonomous vehicle training

Discover how SUPA's specialized data labeling services enhanced an autonomous driving company's models, achieving 95% accuracy

problem
The client needed to enhance their autonomous driving model's accuracy in interpreting vector spaces to meet safety standards and effectively navigate diverse environments.
solution
SUPA provided expert data labeling services, combining rigorous annotator training and human-machine collaboration, along with a dedicated quality control team to ensure high-quality, consistent data.
result
SUPA continues to deliver labeled data to the Client with 95% accuracy, significantly improving the client’s model predictions and meeting tight delivery timelines since 2022.
95% accuracy
Autonomous Vehicles
Computer Vision
Data labeling for autonomous vehicle training

STEM Dataset

Bilingual Multimodal STEM Dataset — a curated collection of 500 Math and Physics questions in Malay and English, some enriched with relevant images.

problem
AI models often struggle with bilingual and multimodal STEM tasks due to a lack of high-quality, domain-specific datasets in languages like Malay and English.
solution
We created a curated dataset of 500 Math and Physics questions in Malay and English, complemented by a public leaderboard to benchmark AI model performance.
result
AI teams now have a reliable resource for fine-tuning and evaluating models on real-world STEM tasks, setting a new standard for bilingual and multimodal AI development.
500 high-quality Math and Physics questions
Evaluation Leaderboard
STEM-focused AI evaluation
STEM Dataset