Active learning for medical image segmentation with stochastic batches
- URL: http://arxiv.org/abs/2301.07670v2
- Date: Fri, 8 Sep 2023 19:36:37 GMT
- Title: Active learning for medical image segmentation with stochastic batches
- Authors: M\'elanie Gaillochet, Christian Desrosiers, and Herv\'e Lombaert
- Abstract summary: To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set.
This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images.
- Score: 13.171801108109198
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The performance of learning-based algorithms improves with the amount of
labelled data used for training. Yet, manually annotating data is particularly
difficult for medical image segmentation tasks because of the limited expert
availability and intensive manual effort required. To reduce manual labelling,
active learning (AL) targets the most informative samples from the unlabelled
set to annotate and add to the labelled training set. On the one hand, most
active learning works have focused on the classification or limited
segmentation of natural images, despite active learning being highly desirable
in the difficult task of medical image segmentation. On the other hand,
uncertainty-based AL approaches notoriously offer sub-optimal batch-query
strategies, while diversity-based methods tend to be computationally expensive.
Over and above methodological hurdles, random sampling has proven an extremely
difficult baseline to outperform when varying learning and sampling conditions.
This work aims to take advantage of the diversity and speed offered by random
sampling to improve the selection of uncertainty-based AL methods for
segmenting medical images. More specifically, we propose to compute uncertainty
at the level of batches instead of samples through an original use of
stochastic batches (SB) during sampling in AL. Stochastic batch querying is a
simple and effective add-on that can be used on top of any uncertainty-based
metric. Extensive experiments on two medical image segmentation datasets show
that our strategy consistently improves conventional uncertainty-based sampling
methods. Our method can hence act as a strong baseline for medical image
segmentation. The code is available on:
https://github.com/Minimel/StochasticBatchAL.git.
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