Annotating medical images for the purposes of training computer vision models is an extremely laborious task that takes time and resources away from expert clinicians. Active
learning (AL) is a machine learning paradigm that mitigates this problem by deliberately
proposing data points that should be labeled in order to maximize model performance. We
propose a novel AL algorithm for segmentation, ALGES, that utilizes gradient embeddings
to effectively select laparoscopic images to be labeled by some external oracle while reducing annotation effort. Given any unlabeled image, our algorithm treats predicted segmentations as truth and computes gradients with respect to the model parameters of the last layer in a segmentation network. The norms of these per-pixel gradient vectors correspond to the magnitude of the induced change in model parameters and contain rich information about the model’s predictive uncertainty. Our algorithm then computes gradients
embeddings in two ways, and we employ a center-finding algorithm with these embeddings
to procure representative and diverse batches in each round of AL. An advantage of
our approach is extensibility to any model architecture and differentiable loss scheme for
semantic segmentation. We apply our approach to a public data set of laparoscopic cholecystectomy images and show that it outperforms current AL algorithms in selecting the
most informative data points for improving the segmentation model. Our code is available
at https://github.com/josaklil-ai/surg-active-learning.
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Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images
Image segmentation is an essential step in biomedical image analysis. In recent years, deep learning models have achieved signi cant success in segmentation. However, deep learning requires the availability of large annotated data to train these models, which can be challenging in biomedical imaging domain. In this paper, we aim to accomplish biomedical image segmentation with limited labeled data using active learning. We present a deep active learning framework that selects additional data points to be annotated by combining U-Net with an efficient and effective query strategy to capture the most uncertain and representative points. This algorithm decouples the representative part by first finding the core points in the unlabeled pool and then selecting the most uncertain points from the reduced pool, which are different from the labeled pool. In our experiment, only 13% of the dataset was required with active learning to outperform the model trained on the entire 2018 MIC-
CAI Brain Tumor Segmentation (BraTS) dataset. Thus, active learning
reduced the amount of labeled data required for image segmentation
without a signi cant loss in the accuracy.
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- Award ID(s):
- 1838730
- NSF-PAR ID:
- 10119080
- Date Published:
- Journal Name:
- MICCAI Workshop on Medical Image Learning with Less Labels and Imperfect Data
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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