Ophthalmic AI Prototyping Study

Apr 17, 2025 · 1 min read

Overview

Exploratory prototyping for interpretable ophthalmic image analysis, combining segmentation, classification, uncertainty estimation, and language-based explanation.

Highlights

  • Investigated uncertainty-aware visual analysis
  • Explored multi-label disease classification pipelines
  • Studied patient-friendly explanation generation

Tech Stack PyTorch, computer-vision segmentation and classification models, and multimodal language models

Medical Imaging Ophthalmology Explainable AI Computer Vision
Dongkun Lee
Authors
Dongkun Lee
Senior Staff Engineer | LLM Inference Optimization | Model Compression

I am a Senior Staff Engineer working on efficient AI systems, with a focus on LLM inference optimization and model compression.

My work spans quantization, pruning, knowledge distillation, sparsity, and decoding algorithms, with an emphasis on reducing deployment cost while preserving model quality.

I hold a Ph.D. in Computer Science from KAIST, with a research background in out-of-distribution detection, anomaly segmentation, uncertainty estimation, generative modeling, and computer vision.