Abstract
Few-shot segmentation has received recent attention because of its promise to segment images containing novel classes based on a handful of annotated examples. Few-shot-based machine learning methods build generic and adaptable models that can quickly learn new tasks. This approach finds potential application in many scenarios that do not benefit from large repositories of labeled data, which strongly impacts the performance of the existing data-driven deep-learning algorithms. This paper presents a few-shot segmentation method for microscopy images that combines a neural-network architecture with a Gaussian-process (GP) regression. The GP regression is used in the latent space of an autoencoder-based segmentation model to learn the distribution of functions from the encoded image representations to the corresponding representation of the segmentation masks in the support set. This regression analysis serves as the prior for predicting the segmentation mask for the query image. The rich latent representation built by the GP using examples in the support set significantly impacts the performance of the segmentation model, demonstrated by extensive experimental evaluation.
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Al-Kofahi, Y., Zaltsman, A., Graves, R., Marshall, W., Rusu, M.: A deep learning-based algorithm for 2-d cell segmentation in microscopy images. BMC Bioinform. 19(365), 1050–1065 (2018)
Chen, X., Zhao, Y., Liu, C.: Medical image segmentation using scalable functional variational Bayesian neural networks with gaussian processes. Neurocomputing 500, 58–72 (2022)
Dawoud, Y., Hornauer, J., Carneiro, G., Belagiannis, V.: Few-shot microscopy image cell segmentation. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12461, pp. 139–154. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67670-4_9
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, vol. 70, pp. 1126–1135 (2017)
Gerhard, S., Funke, J., Martel, J., Cardona, A., Fetter, R.: Segmented anisotropic ssTEM dataset of neural tissue. In: figshare (2013)
Han, L., Yin, Z.: Unsupervised network learning for cell segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 282–292. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_27
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Johnander, J., Edstedt, J., Felsberg, M., Khan, F.S., Danelljan, M.: Dense Gaussian processes for few-shot segmentation (2021)
Kassim, Y.M., Glinskii, O.V., Glinsky, V.V., Huxley, V.H., Palaniappan, K.: Patch-based semantic segmentation for detecting arterioles and venules in epifluorescence imagery. In: IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–5 (2018)
Koch, G.: Siamese neural networks for one-shot image recognition. Master’s thesis, University of Toronto (2015)
Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007)
Liu, D., et al.: Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4243–4252 (2020)
Lucchi, A., Li, Y., Fua, P.: Learning for structured prediction using approximate subgradient descent with working sets. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1987–1994. IEEE (2013)
Mahajan, K., Sharma, M., Vig, L.: Meta-dermdiagnosis: few-shot skin disease identification using meta-learning. In: Computer Vision and Pattern Recognition Workshops. IEEE (2020)
Naylor, P., Laé, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448–459 (2019)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms (2018)
Nishimura, K., Ker, D.F.E., Bise, R.: Weakly supervised cell instance segmentation by propagating from detection response. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 649–657. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_72
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning (2016)
Shaban, A., Shray, Liu, B.Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: British Machine Vision Conference. BMVA Press (2017)
Singh, R., Bharti, V., Purohit, V., Kumar, A., Singh, A.K., Singh, S.K.: MetaMed: few-shot medical image classification using gradient-based meta-learning. Pattern Recogn. 44(2), 1050–1065 (2021)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Snell, J., Zemel, R.S.: Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes. In: International Conference on Learning Representations (2021)
Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., Jia, J.: Prior guided feature enrichment network for few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1050–1065 (2022)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: Few-shot image semantic segmentation with prototype alignment. In: IEEE/CVF International Conference on Computer Vision, pp. 9196–9205 (2019)
Wu, H., Wang, Z., Song, Y., Yang, L., Qin, J.: Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11666–11675 (2022)
Xie, G.S., Liu, J., Xiong, H., Shao, L.: Scale-aware graph neural network for few-shot semantic segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5471–5480 (2021)
Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Visual. 6(3), 283–292 (2018)
Ze, W., Zichen, M., Xiantong, Z., Qiang, Q.: Learning to learn dense gaussian processes for few-shot learning. In: Advances in Neural Information Processing Systems (2021)
Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. In: IEEE/CVF International Conference on Computer Vision, pp. 9586–9594 (2019)
Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C.: CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5212–5221 (2019)
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Saha, S., Choi, O., Whitaker, R. (2022). Few-Shot Segmentation of Microscopy Images Using Gaussian Process. In: Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (eds) Medical Optical Imaging and Virtual Microscopy Image Analysis. MOVI 2022. Lecture Notes in Computer Science, vol 13578. Springer, Cham. https://doi.org/10.1007/978-3-031-16961-8_10
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