OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation
Abstract
Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framework that democratizes access to DL tools in clinical practice. Osteo- CAD delivers end-to-end DL capabilities-from dataset creation and preprocessing to model training and inference-through an integrated and user-friendly interface. To mitigate local hardware constraints, the framework securely connects to remote GPU infrastructures. We validate OsteoCAD's feasibility through a real-world case study in Mexico focused on large bone tumor segmentation. The results demonstrate the framework's ability to enable DL-powered eHealth solutions without demanding ad- vanced technical expertise or complex local configurations.
Cite
@article{arxiv.2607.29266,
title = {OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation},
author = {Maximo Rodriguez-Herrero and Dante D. Sanchez-Gallegos and Heriberto Aguirre-Meneses and Marco Antonio Núñez-Gaona and J. L. Gonzalez-Compean and Jesus Carretero},
journal= {arXiv preprint arXiv:2607.29266},
year = {2026}
}