用联邦学习+YOLOv11实现跨机构脑瘤精准检测，保护隐私且效果更优。
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11
- 基于联邦学习框架，多机构协作训练YOLOv11模型，不共享原始数据。
- 在多个匿名医院的MRI数据上验证，检测准确率显著优于传统方法。
- 适合医疗数据隐私要求高、需跨院合作的脑瘤智能诊断场景。
医学影像诊断中，磁共振成像（MRI）用于脑瘤检测面临两大挑战：数据隐私与高延迟。为解决此问题，本文提出一种融合YOLOv11的联邦学习架构，实现高效精准的脑瘤检测。相比传统集中式学习，该方法在保护各机构原始医疗数据的前提下，支持跨机构协同深度学习模型训练。针对MRI图像特性，对YOLOv11进行适配以精确定位和识别肿瘤区域。模型在多个匿名医疗机构采集的多样化MRI数据上训练与测试，结果表明，本方法在保持高准确性的同时，显著优于现有常规方法。
原文摘要 · Abstract (English)
One of the primary challenges in medical diagnostics is the accurate and efficient use of magnetic resonance imaging (MRI) for the detection of brain tumors. But the current machine learning (ML) approaches have two major limitations, data privacy and high latency. To solve the problem, in this work we propose a federated learning architecture for a better accurate brain tumor detection incorporating the YOLOv11 algorithm. In contrast to earlier methods of centralized learning, our federated learning approach protects the underlying medical data while supporting cooperative deep learning model training across multiple institutions. To allow the YOLOv11 model to locate and identify tumor areas, we adjust it to handle MRI data. To ensure robustness and generalizability, the model is trained and tested on a wide range of MRI data collected from several anonymous medical facilities. The results indicate that our method significantly maintains higher accuracy than conventional approaches.
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