Few-shot segmentation of medical images based on meta-learning with
implicit gradients
- URL: http://arxiv.org/abs/2106.03223v1
- Date: Sun, 6 Jun 2021 19:52:06 GMT
- Title: Few-shot segmentation of medical images based on meta-learning with
implicit gradients
- Authors: Rabindra Khadga, Debesh Jha, Sharib Ali, Steven Hicks, Vajira
Thambawita, Michael A. Riegler, and P{\aa}l Halvorsen
- Abstract summary: We propose to exploit an optimization-based implicit model agnostic meta-learning iMAML algorithm in a few-shot setting for medical image segmentation.
Our approach can leverage the learned weights from a diverse set of training samples and can be deployed on a new unseen dataset.
- Score: 0.48861336570452174
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Classical supervised methods commonly used often suffer from the requirement
of an abudant number of training samples and are unable to generalize on unseen
datasets. As a result, the broader application of any trained model is very
limited in clinical settings. However, few-shot approaches can minimize the
need for enormous reliable ground truth labels that are both labor intensive
and expensive. To this end, we propose to exploit an optimization-based
implicit model agnostic meta-learning {iMAML} algorithm in a few-shot setting
for medical image segmentation. Our approach can leverage the learned weights
from a diverse set of training samples and can be deployed on a new unseen
dataset. We show that unlike classical few-shot learning approaches, our method
has improved generalization capability. To our knowledge, this is the first
work that exploits iMAML for medical image segmentation. Our quantitative
results on publicly available skin and polyp datasets show that the proposed
method outperforms the naive supervised baseline model and two recent few-shot
segmentation approaches by large margins.
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