Deep Active Learning Enhanced Robust Bioimage Analysis

This talk addresses data efficiency and robustness challenges in deep learning applications for optical coherence tomography (OCT) and bioimaging. It shows a comprehensive workflow for data management, annotation, and model training to overcome limitations posed by scarce annotated datasets, small datasets and image variability. Key contributions include an active learning strategy with human-in-the-loop, an efficient image grading framework, software tools for data preparation, and a curated multi-disease OCT dataset. These advancements aim to improving efficiency and human-AI collaboration for biomedical image analysis, benefiting healthcare and research applications.

Kurz-Bio:

Mustafa Arikan is a PhD candidate at UCL's Institute of Ophthalmology, combining computer science expertise with biomedical research. He holds degrees from Uni Wien and TU Wien in computer science and software engineering. He currently works as a Senior Scientist at Tecan, a Swiss-based company providing lab automation solutions. His research focuses on human-AI collaboration, deep active learning, and biomedical data analysis, bridging technology and healthcare advancements.