Abstract
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly in remote areas. Our research addresses this issue by implementing Neural Cellular Automata (NCA) training directly on smartphones for accessible X-ray lung segmentation. We confirm the practicality and feasibility of deploying and training these advanced models on five Android devices, improving medical diagnostics accessibility and bridging the tech divide to extend machine learning benefits in medical imaging to low- and middle-income countries (LMICs). We further enhance this approach with an unsupervised adaptation method using the novel Variance-Weighted Segmentation Loss (VWSL), which efficiently learns from unlabeled data by minimizing the variance from multiple NCA predictions. This strategy notably improves model adaptability and performance across diverse medical imaging contexts without the need for extensive computational resources or labeled datasets, effectively lowering the participation threshold. Our methodology, tested on three multisite X-ray datasets-Padchest, ChestX-ray8, and MIMIC-III-demonstrates improvements in segmentation Dice accuracy by 0.7 to 2.8%, compared to the classic Med-NCA. Additionally, in extreme cases where no digital copy is available and images must be captured by a phone from an X-ray lightbox or monitor, VWSL enhances Dice accuracy by 5–20%, demonstrating the method’s robustness even with suboptimal image sources.
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References
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/, software available from tensorflow.org
Adam, D.: Medical AI could be’dangerous’ for poorer nations, who warns. Nature (2024)
Ajani, T.S., Imoize, A.L., Atayero, A.A.: An overview of machine learning within embedded and mobile devices–optimizations and applications. Sensors 21(13), 4412 (2021)
Bustos, A., Pertusa, A., Salinas, J.M., De La Iglesia-Vaya, M.: Padchest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Frija, G., Blažić, I., Frush, D.P., Hierath, M., Kawooya, M., Donoso-Bach, L., Brkljačić, B.: How to improve access to medical imaging in low-and middle-income countries? EClinicalMedicine 38, 101034 (2021)
Gaggion, N., Mosquera, C., Mansilla, L., Aineseder, M., Milone, D.H., Ferrante, E.: Chexmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images. arXiv preprint arXiv:2307.03293 (2023)
Gilpin, W.: Cellular automata as convolutional neural networks. Physical Review E 100(3), 032402 (2019)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Sci. Data 3(1), 1–9 (2016)
Kalkhof, J., González, C., Mukhopadhyay, A.: Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging, IPMI 2023, LNCS, vol. 13939, pp. 705–16. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34048-2_54
Kalkhof, J., Mukhopadhyay, A.: M3D-NCA: robust 3d segmentation with built-in quality control. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, MICCAI 2023, LNCS, vol. 14222, pp. 169–178. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43898-1_17
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
McCool, J., Dobson, R., Whittaker, R., Paton, C.: Mobile health (mhealth) in low-and middle-income countries. Annual Review of Public Health 43, 525–539 (2022)
Mordvintsev, A., Randazzo, E., Niklasson, E., Levin, M.: Growing neural cellular automata. Distill 5(2), e23 (2020)
Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Perez-Garcia, F.: fepegar/unet: First published version of PyTorch U-Net, October 2019. https://doi.org/10.5281/zenodo.3522306
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, MICCAI 2015, LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
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Kalkhof, J., Ranem, A., Mukhopadhyay, A. (2024). Unsupervised Training of Neural Cellular Automata on Edge Devices. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_47
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