Brain tumor detection in multiplane Magnetic Resonance Imaging (MRI) slices is a challenging task due to the various appearances and relationships in the structure of the multiplane images. In this paper, we propose a new You Only Look Once (YOLO)-based detection model that incorporates Pretrained Knowledge (PK), called PK-YOLO, to improve the performance for brain tumor detection in multiplane MRI slices. To our best knowledge, PK-YOLO is the first pretrained knowledge guided YOLO-based object detector. The main components of the new method are a pretrained pure lightweight convolutional neural network-based backbone via sparse masked modeling, a YOLO architecture with the pretrained backbone, and a regression loss function for improving small object detection. The pretrained backbone allows for feature transferability of object queries on individual plane MRI slices into the model encoders, and the learned domain knowledge base can improve in-domain detection. The improved loss function can further boost detection performance on small-size brain tumors in multiplanar two-dimensional MRI slices. Experimental results show that the proposed PK-YOLO achieves competitive performance on the multiplanar MRI brain tumor detection datasets compared to state-of-the-art YOLO-like and DETR-like object detectors. The code is available at https://github.com/mkang315/PK-YOLO.

翻译：在多平面磁共振成像（MRI）切片中进行脑肿瘤检测是一项具有挑战性的任务，这是由于多平面图像结构中存在多样的外观特征与关联关系。本文提出了一种新的基于“你只看一次”（YOLO）的检测模型，该模型融合了预训练知识（PK），称为PK-YOLO，旨在提升多平面MRI切片中脑肿瘤检测的性能。据我们所知，PK-YOLO是首个基于预训练知识引导的YOLO目标检测器。该新方法的主要组成部分包括：一个通过稀疏掩码建模预训练的纯轻量级卷积神经网络骨干网络、一个搭载该预训练骨干网络的YOLO架构，以及一个用于改进小目标检测的回归损失函数。预训练的骨干网络使得模型编码器能够迁移来自单个平面MRI切片中目标查询的特征，而学习到的领域知识库可以提升领域内检测性能。改进的损失函数能进一步促进在多平面二维MRI切片中对小尺寸脑肿瘤的检测性能。实验结果表明，与最先进的类YOLO及类DETR目标检测器相比，所提出的PK-YOLO在多平面MRI脑肿瘤检测数据集上取得了具有竞争力的性能。代码可在 https://github.com/mkang315/PK-YOLO 获取。