Abstract
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 this https URL.
Abstract (translated)
脑肿瘤在多平面磁共振成像（MRI）切片中的检测是一项挑战性任务，因为多平面图像的结构具有各种外观和关系。本文提出了一种新的基于“仅看一次”(YOLO) 的检测模型，该模型结合了预训练知识(PK)，称为PK-YOLO，以提高在多平面MRI切片中脑肿瘤检测的表现。据我们所知，PK-YOLO是首个由预训练知识引导的YOLO基础目标检测器。新方法的主要组成部分包括通过稀疏掩模建模预先训练的纯轻量级卷积神经网络主干、带有预训练主干的YOLO架构以及用于改善小物体检测的回归损失函数。预训练的主干允许将单个平面MRI切片中的对象查询特征传递到模型编码器中，而学到的领域知识库可以提升域内检测效果。改进后的损失函数能够进一步提高多平面二维MRI切片中小尺寸脑肿瘤的检测性能。实验结果显示，所提出的PK-YOLO在多平面MRI脑肿瘤检测数据集上的表现与最先进的YOLO类和DETR类目标检测器相比具有竞争力。代码可在以下链接获取：[此https URL]。
URL
https://arxiv.org/abs/2410.21822
