Realism in Action：基于YOLOv8和DeiT的医学图像脑肿瘤异常感知诊断
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
AI总结：
针对脑肿瘤低发病率被忽略的问题，提出基于YOLOv8n和DeiT知识蒸馏的异常鲁棒检测分类框架，并引入PTP患者级评估指标，在真实不平衡数据上实现高效且鲁棒的端到端诊断。
摘要：
由于临床发病率低，脑肿瘤的可靠诊断仍然具有挑战性。然而，大多数已提出的方法忽略了这一低发病率。我们提出了一个受临床启发的异常鲁棒肿瘤检测与分类框架。检测利用在真实不平衡数据集（肿瘤与正常比例为1:9；来自81名患者的30,000张MRI切片）上微调的YOLOv8n。此外，我们提出了一种新颖的Patient-to-Patient (PTP)指标，用于评估患者层面的诊断可靠性。分类采用知识蒸馏：从ResNet152教师模型中蒸馏出Data Efficient Image Transformer (DeiT)学生模型。蒸馏后的ViT在20个epoch内达到0.92的F1-score，接近教师模型性能（F1=0.97），同时显著减少了计算资源。该端到端框架在临床代表性的异常分布数据中表现出高鲁棒性，为临床实际情况提供了可行工具。
英文摘要：
Reliable diagnosis of brain tumors remains challenging due to low clinical incidence rates of such cases. However, this low rate is neglected in most of proposed methods. We propose a clinically inspired framework for anomaly-resilient tumor detection and classification. Detection leverages YOLOv8n fine-tuned on a realistically imbalanced dataset (1:9 tumor-to-normal ratio; 30,000 MRI slices from 81 patients). In addition, we propose a novel Patient-to-Patient (PTP) metric that evaluates diagnostic reliability at the patient level. Classification employs knowledge distillation: a Data Efficient Image Transformer (DeiT) student model is distilled from a ResNet152 teacher. The distilled ViT achieves an F1-score of 0.92 within 20 epochs, matching near teacher performance (F1=0.97) with significantly reduced computational resources. This end-to-end framework demonstrates high robustness in clinically representative anomaly-distributed data, offering a viable tool that adheres to realistic situations in clinics.
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