Explainable AI for Brain Tumour Auditing and Tagging

脑瘤是一个严重的健康问题；早期发现对成功治疗至关重要。磁共振成像（MRI）是识别脑肿瘤最准确的非手术方法，但由放射科医生手工分析既耗时又容易出错。深度学习模型在检测脑肿瘤方面显示出了令人鼓舞的结果，但它们的黑盒子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人工智能模型来自主检测，使用MRI图像分割脑肿瘤，并为决策提供简明的解释。BAT可以用于临床审计或标记，而不需要患者的个人信息。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。

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.