In the field of medical sciences, reliable detection and classification of brain tumors from images remains a formidable challenge due to the rarity of tumors within the population of patients. Therefore, the ability to detect tumors in anomaly scenarios is paramount for ensuring timely interventions and improved patient outcomes. This study addresses the issue by leveraging deep learning (DL) techniques to detect and classify brain tumors in challenging situations. The curated data set from the National Brain Mapping Lab (NBML) comprises 81 patients, including 30 Tumor cases and 51 Normal cases. The detection and classification pipelines are separated into two consecutive tasks. The detection phase involved comprehensive data analysis and pre-processing to modify the number of image samples and the number of patients of each class to anomaly distribution (9 Normal per 1 Tumor) to comply with real world scenarios. Next, in addition to common evaluation metrics for the testing, we employed a novel performance evaluation method called Patient to Patient (PTP), focusing on the realistic evaluation of the model. In the detection phase, we fine-tuned a YOLOv8n detection model to detect the tumor region. Subsequent testing and evaluation yielded competitive performance both in Common Evaluation Metrics and PTP metrics. Furthermore, using the Data Efficient Image Transformer (DeiT) module, we distilled a Vision Transformer (ViT) model from a fine-tuned ResNet152 as a teacher in the classification phase. This approach demonstrates promising strides in reliable tumor detection and classification, offering potential advancements in tumor diagnosis for real-world medical imaging scenarios.

翻译：在医学科学领域，由于肿瘤在患者群体中的罕见性，从图像中可靠地检测和分类脑肿瘤仍是一项严峻挑战。因此，在异常场景中检测肿瘤的能力对于确保及时干预和改善患者预后至关重要。本研究利用深度学习技术解决这一问题，在复杂情况下实现脑肿瘤的检测与分类。来自国家脑图谱实验室的精选数据集包含81名患者，其中30例肿瘤病例和51例正常病例。检测与分类流程被分为两个连续任务。检测阶段涉及全面的数据分析和预处理，旨在调整各类别的图像样本数量和患者数量至异常分布（每1例肿瘤对应9例正常），以符合真实世界场景。随后，除常规测试评估指标外，我们采用了一种名为患者对患者的新型性能评估方法，专注于模型的真实评估。在检测阶段，我们对YOLOv8n检测模型进行微调以定位肿瘤区域。后续测试与评估在通用评估指标和PTP指标上均取得了具有竞争力的性能。此外，利用数据高效图像Transformer模块，我们在分类阶段将微调后的ResNet152作为教师模型，提炼出视觉Transformer模型。该方法在可靠肿瘤检测与分类方面取得了显著进展，为真实医学影像场景中的肿瘤诊断提供了潜在的技术突破。