Explainable AI for Brain Tumour Auditing and Tagging

用于脑肿瘤审核和标记的可解释人工智能

• 批准号: 10072780
• 金额: $ 6.36万
• 依托单位: THERMOSOL LIMITED
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10072780
• 关键词: Explainable; AI; Brain; Tumour; Auditing

Brain tumors are a serious health issue; early detection is crucial for successful treatment. Magnetic Resonance Imaging (MRI) is the most accurate non-surgical method for identifying brain tumors, but manual analysis by radiologists is time-consuming and prone to errors. Deep learning models have shown promising results in detecting brain tumors, but their black-box nature and privacy concerns restrict their adoption in real-world scenarios. To address these issues, a new framework called BAT is proposed, which uses state-of-the-art explainable AI models to detect autonomously, segment brain tumors using MRI images, and provide concise explanations for the decision. BAT can be used for clinical auditing or tagging without requiring patient personal information. The key innovation of BAT is its ability to automate tumor detection, segmentation, and stage prediction while ensuring anonymity and interpretability.

脑肿瘤是一个严重的健康问题;早期发现是成功治疗的关键。磁共振成像（MRI）是识别脑肿瘤的最准确的非手术方法，但放射科医生的手动分析耗时且容易出错。深度学习模型在检测脑肿瘤方面已经显示出了很好的效果，但它们的黑盒性质和隐私问题限制了它们在现实世界中的应用。为了解决这些问题，提出了一个名为BAT的新框架，该框架使用最先进的可解释AI模型来自主检测，使用MRI图像分割脑肿瘤，并为决策提供简洁的解释。BAT可用于临床审计或标记，而无需患者个人信息。BAT的关键创新在于其自动化肿瘤检测、分割和阶段预测的能力，同时确保匿名性和可解释性。