  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.
