Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
Date
2023
Authors
Dawoud, Y.
Bouazizi, A.
Ernst, K.
Carneiro, G.
Belagiannis, V.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2023), 2023, pp.3557-3566
Statement of Responsibility
Youssef Dawoud, Arij Bouazizi, Katharina Ernst, Gustavo Carneiro, Vasileios Belagiannis
Conference Name
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2 Jan 2023 - 7 Jan 2023 : Waikoloa, HI, USA)
Abstract
In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a support set comprising a few randomly selected and annotated training target images. In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process. Our approach involves a new scoring function to find informative unlabelled target images. In particular, we propose to measure the consistency in the model predictions on target images against specific data augmentations. However, we observe that the model trained with source datasets does not reliably evaluate consistency on target images. To alleviate this problem, we propose novel self-supervised pretext tasks to compute the scores of unlabelled target images. Finally, the top few images with the least consistency scores are added to the support set for oracle (i.e., expert) annotation and later used to fine-tune the model to the target images. In our evaluations that involve the segmentation of five different types of cell images, we demonstrate promising results on several target test sets compared to the random selection approach as well as other selection approaches, such as Shannon’s entropy and Monte-Carlo dropout.
School/Discipline
Dissertation Note
Provenance
Description
Date Added to IEEE Xplore: 06 February 2023
Access Status
Rights
©2023 IEEE