Summary 🧬
Research paper accepted at ESANN 2022 that benchmarks deep segmentation backbones for renal glomeruli detection on mouse histology.
The journey 🧭
- The story begins with my undergraduate thesis, where I leveraged DeepLab V2 to segment renal sections of mice.
- For this follow-up, I introduced MobileNet to both save computation and compare performance trade-offs between heavy and lite encoders.
- The analysis focused on precision vs. speed, exploring how lightweight encoders can maintain accuracy while enabling faster inference for eventual deployment in lab gadgets.
Outcomes & takeaways 📈
- The paper is publicly available, with the full method and results shared at the conference link.
- Code and trained models are published on GitHub, making it easy to reproduce or extend the segmentation pipeline.
- The project reaffirmed my interest in biomedical applications of deep learning and taught me how to document results for a scientific audience.