AI Scientist | Computational Pathology & Multi-Omics

Bridging histopathology, proteomics, and clinical signals with AI.

I am an AI researcher working at the intersection of computational pathology and
multi-omics modeling. My research focuses on extracting phenotypic representations
from gigapixel whole-slide histopathology images (WSIs) and connecting them to
proteomic, molecular, and clinical outcomes for translational research and drug discovery.

My work emphasizes scalable WSI analysis, representation learning, and multimodal
integration toward clinically meaningful and interpretable AI systems.

• AAAI 2026 Workshop (W3PHIAI)
  G2L: From Giga-Scale to Cancer-Specific Pathology Foundation Models via Knowledge Distillation
  → Third Author
  → Contributed to gigapixel WSI distillation strategy and experimental analysis

• KPI Challenge 2024
  🥈 2nd Place – Glomerular Segmentation (Whole-Slide Level)
  → Patch-to-slide level segmentation pipeline with strong generalization

• Scientific Reports (2025)
  Assessing the risk of recurrence in early-stage breast cancer through H&E-stained WSIs
  → Vision-only prognostic modeling without molecular assays

My research interests center on problems where visual phenotypes can be translated
into molecular or clinical insights, including:

• Computational Pathology (WSI, MIL, segmentation)
• Representation Learning for Medical Images
• Vision–Proteomics / Multi-Omics Integration
• Drug Response & Cell Perturbation Modeling
• Model Calibration, Uncertainty, and Interpretability

• Deep Learning & Computer Vision
  PyTorch, MONAI, TorchVision, OpenCV, scikit-image

• Data Science & MLOps
  Python, FastAPI, Docker, Linux, Git

• Domains
  Medical Imaging, Multi-Modal Learning, Translational AI, Drug Discovery

• G2L: From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation
  W3PHIAI @ AAAI 2026 (Workshop) — 3rd Author
  https://arxiv.org/abs/2510.11176

• KPI Challenge 2024: Advancing Glomerular Segmentation from Patch-to-Slide-Level
  arXiv preprint, 2025
  https://arxiv.org/abs/2502.07288

• MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer
  Bioengineering, 2024

• Supervised Contrastive Embedding for Medical Image Segmentation
  IEEE Access, 2022

• Assessing the risk of recurrence in early-stage breast cancer through H&E-stained whole slide images
  Scientific Reports (Nature Publishing Group), 2025
  https://www.nature.com/articles/s41598-025-16679-x

• AI-driven Digital Pathology in Urological Cancers: Current Trends and Future Directions
  Pattern Recognition in Life Sciences / Prostate International, 2025

• Predicting Protein Receptor Status from H&E-stained Images in Breast Cancer
  AACR Annual Meeting, 2023 (Abstract)

• Automatic Histological Grading of Breast Cancer Resection Tissue
  USCAP Annual Meeting, 2022 (Abstract)

• M.S. in Data Science, Seoul National University of Science and Technology
  Thesis: Utilizing Contrastive Loss to Improve Segmentation Model Performance

• B.S. in Information Security, Daejeon University

• 🥈 2nd Place, KPI Challenge 2024
  (Glomerular Segmentation, Whole-Slide Level)

• 🏡 7th Place, Dacon × Zigbang Apartment Price Prediction

• 📧 Email: rjsrb365@gmail.com
• 🔗 LinkedIn: https://www.linkedin.com/in/geongyu-lee
• 🎓 Google Scholar: https://scholar.google.com/citations?user=43BuluYAAAAJ