Leonid Mill
Scholar

Leonid Mill

Google Scholar ID: Ng1jBCEAAAAJ
MIRA Vision
Homepage↗Google Scholar↗
Citations & Impact
All-time
Citations
650
 
H-index
6
 
i10-index
5
 
Publications
20
 
Co-authors
0
 
Contact
Emailinfo@mira.visionLinkedInOpen ↗
Publications
2 items
A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
2025
Cited
0
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
2025
Cited
0
Resume (English only)
Academic Achievements
  • Addressed six core problems faced by traditional AI training data: expensive data generation, massive data volumes, domain shift, privacy and regulation restrictions, high costs for annotations, etc.
Research Experience
  • Developed fully parametric synthetic AI systems that can automatically analyze pathological image data without the need for real patient data training.
Background
  • Focused on revolutionizing medical diagnostics through artificial intelligence, particularly using synthetic data for pathology image analysis.
Miscellany
  • Aims to make medical diagnostics more sustainable, accessible, and efficient; reduces costs and resource consumption through synthetic training data, eliminates privacy concerns, and enhances the speed and precision of analysis.
Co-authors
0 total
Co-authors: 0 (list not available)