Volunteer for a startup using AI to save lives from cervical cancer! 🩺
MednTech provides an Android AI tool that analyzes cervical images to support frontline screening decisions and improve health outcomes for women in low-resource settings.
Cervical cancer is preventable, yet it claimed 350,000 lives in 2022—94% of which occurred in low- and middle-income countries. In sub-Saharan Africa, it remains the leading cause of cancer death among women, yet screening rates are as low as 14% due to a lack of infrastructure and trained providers. While visual inspection is the WHO-recommended screening method, its interpretation is highly subjective and inconsistent, leaving frontline workers without the necessary quality assurance to make life-saving decisions at scale.
MednTech addresses this crisis through CerviScanner, an AI-based diagnostic support tool that embeds expert-level computer vision directly into the existing clinical workflow. Using a standard Android smartphone, frontline health workers can capture cervical images and receive real-time classification and confidence scores without the need for specialized labs or onsite specialists. Built on locally collected, expert-labeled data, the app aligns with national protocols and flags uncertain cases to ensure clinicians remain in control of the final diagnosis. By providing objective, AI-assisted feedback where it is needed most, CerviScanner empowers healthcare providers to deliver high-quality, accessible screening that can prevent thousands of unnecessary deaths.
Role (Volunteer, unpaid): Computer Vision Engineer
Role Description: We are early-stage, moving fast, and building something that matters. You will have real ownership over technical decisions that directly shape a tool going into the field. Cervical cancer is one of the most preventable cancers in the world, yet it remains a leading cause of cancer death in low-resource settings simply because screening does not reach the people who need it. The tool you help build changes that. If you want your code to save lives, this is that opportunity!
Role Overview Your role will be to aid in iterative and ongoing work to optimize computer vision techniques for extracting meaningful features from cervical images and improving model performance through domain-specific image understanding.
Key Responsibilities
- Develop domain-specific feature extraction pipelines
- Experiment with:
- Transfer learning (EfficientNet, ResNet, Swin Transformers)
- Feature engineering vs end-to-end learning
- Build and test augmentation strategies tailored to medical imaging
- Analyze model failures and identify visual patterns causing misclassification
- Collaborate across preprocessing and modeling pipelines
Required Skills
- Strong experience in computer vision and deep learning
- Proficiency in PyTorch or TensorFlow
- Understanding of CNNs and Vision Transformers
- Experience debugging model performance using visual analysis
Preferred Qualifications
- Experience with medical imaging or diagnostic systems
- Knowledge of interpretability methods (Grad-CAM, saliency maps)
- Familiarity with small dataset optimization techniques
- All roles are highly collaborative and will work closely across the MednTech AI pipeline
- Experience with healthcare AI, low-resource environments, or global health applications is a strong plus
- Candidates should be comfortable working in fast-paced, early-stage environments
Time Commitment: Volunteer 4-6 hours per week for 1-2 months remotely 💻
If you want to make change happen, apply to volunteer with MednTech now!
