Abstract
Cell counting in microscopy images is vital in medicine and biology but extremely tedious and time-consuming to perform manually. While automated methods have advanced in recent years, state-of-the-art approaches tend to increasingly complex model designs. In this paper, we propose a conceptually simple yet effective decoupled learning scheme for automated cell counting, consisting of separate counter and localizer networks. In contrast to jointly learning counting and density map estimation, we show that decoupling these objectives surprisingly improves results. The counter operates on intermediate feature maps rather than pixel space to leverage global context and produce count estimates, while also generating coarse density maps. The localizer then reconstructs high-resolution density maps that precisely localize individual cells, conditional on the original images and coarse density maps from the counter. Besides, to boost counting accuracy, we further introduce a global message passing module to integrate cross-region patterns. Extensive experiments on four datasets demonstrate that our approach, despite its simplicity, challenges common practice and achieves state-of-the-art performance by significant margins. Our key insight is that decoupled learning alleviates the need to learn counting on high-resolution density maps directly, allowing the model to focus on global features critical for accurate estimates. Code is available at https://github.com/MedAITech/DCL.
Z. Zheng and Y. Shi—Equal contribution.
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References
Guo, Y., Stein, J., Wu, G., Krishnamurthy, A.: SAU-Net: A universal deep network for cell counting. In: ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pp. 299–306. (2019)
Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108. (2019)
Cheng, Z.Q., Li, J.X., Dai, Q., Wu, X., Hauptmann, A.G.: Learning spatial awareness to improve crowd counting. In: IEEE/CVF International Conference on Computer Vision, pp. 6152–6161. (2019)
Wang, Z., Yin, Z.: Cell counting by a location-aware network. In: Machine Learning in Medical Imaging, pp. 120–129. (2021)
ldughayfiq, B., Ashfaq, F., Jhanjhi, N.Z., Humayun, M.: YOLOv5-FPN: A robust framework for multi-sized cell counting in fluorescence images. Diagnostics 13(13), 2280 (2023)
Ma, Y., Sanchez, V., Guha, T.: FusionCount: Efficient crowd counting via multiscale feature fusion. In: IEEE International Conference on Image Processing, pp. 3256–3260. (2022)
Lin, W., Chan, A.B.: Optimal transport minimization: Crowd localization on density maps for semi-supervised counting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21663–21673. (2023)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Marsden, M., McGuinness, K., Little, S., Keogh, C.E., O’Connor, N.E.: People, penguins and petri dishes: Adapting object counting models to new visual domains and object types without forgetting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8070–8079. (2018)
Cohen, J.P., Boucher, G., Glastonbury, C.A., Lo, H.Z., Bengio, Y.: Count-ception: Counting by fully convolutional redundant counting. In: IEEE International Conference on Computer Vision Workshops, pp. 18–26. (2017)
Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 276–283. (2015)
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, pp. 1324–1332. (2010)
Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752. (2017)
Liu, J., Gao, C., Meng, D., Hauptmann, A.G.: DecideNet: Counting varying density crowds through attention guided detection and density estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5197–5206. (2018)
Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597. (2016)
Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks.: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018)
Li, Y., Zhang, X., Chen, D.: CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1091–1100. (2018)
Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207. (2019)
Wang, B., Liu, H., Samaras, D., Nguyen, M.H.: Distribution matching for crowd counting. In: Advances in Neural Information Processing Systems, pp. 1595–1607. (2020)
Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? Scale selection for crowd counting. In: AAAI Conference on Artificial Intelligence, pp. 2576–2583. (2021)
Lu, H., Liu, L., Wang, H., Cao, Z.: Counting crowd by weighing counts: A sequential decision-making perspective. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 5141–5154 (2022)
Zhang, S., Yang, L., Mi, M.B., Zheng, X., Yao, A.: Improving deep regression with ordinal entropy. arXiv preprint arXiv:2301.08915 (2023)
Ranasinghe, Y., Nair, N.G., Bandara, W.G.C., Patel, V.M.: Diffuse-Denoise-Count: Accurate crowd-counting with diffusion models. arXiv preprint arXiv:2303.12790 (2023)
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Zheng, Z., Shi, Y., Li, C., Hu, J., Zhu, X.X., Mou, L. (2024). Rethinking Cell Counting Methods: Decoupling Counting and Localization. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15004. Springer, Cham. https://doi.org/10.1007/978-3-031-72083-3_39
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