Knowing What to Label for Few Shot Microscopy Image Cell Segmentation

Dawoud Y, Bouazizi A, Ernst K, Carneiro G, Belagiannis V (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 3557-3566

Conference Proceedings Title: Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Event location: Waikoloa, HI, USA

ISBN: 9781665493468

DOI: 10.1109/WACV56688.2023.00356

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.

Authors with CRIS profile

Vasileios Belagiannis Professur für Maschinelles Lernen in der Signalverarbeitung

Involved external institutions

University of Surrey GB United Kingdom (GB) Universitätsklinikum Ulm DE Germany (DE) Universität Ulm DE Germany (DE)

How to cite

APA:

Dawoud, Y., Bouazizi, A., Ernst, K., Carneiro, G., & Belagiannis, V. (2023). Knowing What to Label for Few Shot Microscopy Image Cell Segmentation. In Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 (pp. 3557-3566). Waikoloa, HI, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Dawoud, Youssef, et al. "Knowing What to Label for Few Shot Microscopy Image Cell Segmentation." Proceedings of the 23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023, Waikoloa, HI, USA Institute of Electrical and Electronics Engineers Inc., 2023. 3557-3566.

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