A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor
Yin Lin Elena De Martin Giacomo Conte Domenico Aquino Cristiana Pedone Alberto Redaelli Riccardo Barbieri Laura Fariselli Simona Ferrante
作者信息
Abstract
Artificial intelligence and radiomics are increasingly used in brain tumor research, yet their translation into clinical practice remains limited by fragmented workflows, poor transparency, and weak integration with end users' needs. We present the first version of a scalable web-based visual analytics system designed to support radiomics-driven machine learning inference in neuro-oncology. The platform integrates three core functions within a single interface: cohort management from structured clinical tables, radiomic feature extraction from medical images and segmentation masks, and guarded inference with pre-trained machine learning models. The system was developed through an iterative user-centred design process and evaluated on both a public glioblastoma dataset and a proprietary clinical cohort. A key contribution is the explicit exposure of intermediate workflow artifacts, which improves traceability, interpretability, and responsible use of AI. By combining portability, inspectability, and deployment simplicity, the proposed framework offers a practical foundation for clinically oriented AI applications in brain tumor analysis.引用本文复制引用
Yin Lin,Elena De Martin,Giacomo Conte,Domenico Aquino,Cristiana Pedone,Alberto Redaelli,Riccardo Barbieri,Laura Fariselli,Simona Ferrante.A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor[EB/OL].(2026-07-29)[2026-08-11].https://arxiv.org/abs/2607.26834.学科分类
医学研究方法/肿瘤学/计算技术、计算机技术
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