Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
This project aims to develop a complete Active Learning System for chest X-ray image classification, designed to automate data preparation, streamline model training, and reduce the manual effort required for medical image labeling. The system establishes a structured and scalable pipeline that moves from raw data ingestion to automated decision-making, incorporating dataset indexing, patient-aware splitting, preprocessing, configuration management, and validation to ensure data flows reliably through the system. The model component uses CNNs to generate baseline diagnostic predictions across chest pathologies. Active learning strategies are then applied to identify the most informative unlabeled images, enabling iterative retraining that improves model performance while minimizing labeling cost. Proposed strategies for training improvements include transfer learning with a pretrained model. Evaluation tools such as a dashboard to track performance would also enable a reproducible, clinically relevant image workflow. The final system would deliver a reproducible framework capable of managing large medical imaging datasets, selecting high-value samples for annotation, and continuously refining classification accuracy over time.
Included in
UC-1222 Active Learning System for Labeling Chest X-rays
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
This project aims to develop a complete Active Learning System for chest X-ray image classification, designed to automate data preparation, streamline model training, and reduce the manual effort required for medical image labeling. The system establishes a structured and scalable pipeline that moves from raw data ingestion to automated decision-making, incorporating dataset indexing, patient-aware splitting, preprocessing, configuration management, and validation to ensure data flows reliably through the system. The model component uses CNNs to generate baseline diagnostic predictions across chest pathologies. Active learning strategies are then applied to identify the most informative unlabeled images, enabling iterative retraining that improves model performance while minimizing labeling cost. Proposed strategies for training improvements include transfer learning with a pretrained model. Evaluation tools such as a dashboard to track performance would also enable a reproducible, clinically relevant image workflow. The final system would deliver a reproducible framework capable of managing large medical imaging datasets, selecting high-value samples for annotation, and continuously refining classification accuracy over time.