Multimodal AI-based computer-aided diagnosis for gastrointestinal endoscopic biopsies (Seegene Medical Foundation). Multiclass and multi-resolution MIL, priority-aware mistake severity training, and decision making under uncertainty.

Next-generation medical diagnosis system based on AI (Seegene Medical Foundation). Whole-slide segmentation, weakly supervised microscopy pathology classification, microscopy diagnosis team manager.

Safety monitoring model based on IoT for subminiature sensors (IITP). Time-series modeling and anomaly detection in sequential data.

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Based on digital whole-slide images (WSIs) of colon tissue specimens, we developed a diagnostic model tailored for non-malignant cases. In this project, our primary focus was placed on: (a) pre-processing massive WSI datasets, (b) building a high-fidelity model capable of expert-level collaboration with pathologists, (c) optimizing the model's diagnostic prioritization for specimens, and (d) enhancing the model's rejection capabilities for uncertain estimations. Serving as the project manager, I not only led the entire lifecycle but also spearheaded the technical solutions for areas (a) through (d), which ultimately resulted in the successful conclusion of the project and peer-reviewed publicatios.

This project aimed to classify gastric and colorectal biopsy samples exhibiting benign conditions versus dysplasia within digital pathology workflows. We pioneered approaches to efficiently train models on gigapixel Whole Slide Images (WSIs) while ensuring robust adaptation to newly integrated datasets. Notably, I served as the Project Manager (PM) and lead developer for a follow-up study focused on diagnostic models for noisy, microscope-based images. In my role as PM, I successfully translated Seegene’s business requirements into concrete technical specifications, steering the team to deliver high-impact engineering outcomes. Leveraging the insight that manual microscopic observations by pathologists function as time-series data, I formulated a novel distance metric based on Dynamic Time Warping (DTW). This work was successfully published at ISBI 2025. Our developed solution is currently deployed in clinical settings to cross-verify pathologists' diagnostic decisions.

Time-series modeling and anomaly detection for sequential sensor data in industrial safety monitoring.