Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 44 references that are in the map. First posted on arXiv 18 Nov 2022.
Builds on
20 of 44 references.
References · 20
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
- 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
- 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
- 2018On First-Order Meta-Learning Algorithmscited by 1,018
- 2017Deep Bayesian Active Learning with Image Datacited by 819
- 20203D Self-Supervised Methods for Medical Imagingcited by 134
- 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
8 more
- 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
- 2017Cost-Effective Active Learning for Melanoma Segmentationcited by 41
- 2018Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learningcited by 36
- 2019Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogatecited by 28
- 2017Self-Supervised Learning for Spinal MRIscited by 24
- 2020Few-Shot Microscopy Image Cell Segmentationcited by 11
- 2021MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentationcited by 6
- 2022Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentationcited by 2
Built on by
Citing papers per year: 2023: 2.
2 papers.