Breast Cancer Detection
Medical image detection workflow using YOLOv8 and evaluation-focused training.
BCD
Breast Cancer Detection
Case study visual placeholder
Architecture flow
1 layersProblem
Medical image detection models require careful data preparation and evaluation to avoid misleading performance claims.
My Role
Built the computer vision workflow from data preparation through model training and evaluation review.
Solution
A YOLOv8 training pipeline handles preprocessing, augmentation, model training, and precision-recall focused evaluation.
Stack
Case study
Problem
Medical image detection models require careful data preparation and evaluation to avoid misleading performance claims.
Proof signal
YOLOv8 · Medical imagery · Augmentation · Precision-recall tuning
My Role
Built the computer vision workflow from data preparation through model training and evaluation review.
Core product work
- Detection training
- Augmentation
- Model evaluation
Solution
A YOLOv8 training pipeline handles preprocessing, augmentation, model training, and precision-recall focused evaluation.
A YOLOv8 training pipeline preprocesses medical images, applies automated augmentation, trains detection models, and evaluates outputs with precision-recall focused metrics.
Architecture Highlights
Image preprocessing and augmentation
YOLOv8 model training workflow
Precision-recall focused evaluation
Challenges and Tradeoffs
- Avoiding overconfident medical-model claims.
- Treating dataset quality and evaluation as core model concerns.
Impact / Outcome
- Built an end-to-end computer vision workflow for medical imagery experimentation.
- Kept evaluation framing focused on precision-recall tradeoffs instead of unsupported headline claims.
Learnings
- High-stakes ML projects need cautious language and careful validation.
- Evaluation choices matter as much as model architecture.
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