Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
Labeling has always been expensive in the medical context, which has hindered
related deep learning application. Our work introduces active learning in
surgical video frame selection to construct a high-quality, affordable
Laparoscopic Cholecystectomy dataset for semantic segmentation. Active learning
allows the Deep Neural Networks (DNNs) learning pipeline to include the dataset
construction workflow, which means DNNs trained by existing dataset will
identify the most informative data from the newly collected data. At the same
time, DNNs' performance and generalization ability improve over time when the
newly selected and annotated data are included in the training data. We
assessed different data informativeness measurements and found the deep
features distances select the most informative data in this task. Our
experiments show that with half of the data selected by active learning, the
DNNs achieve almost the same performance with 0.4349 mean Intersection over
Union (mIoU) compared to the same DNNs trained on the full dataset (0.4374
mIoU) on the critical anatomies and surgical instruments.
