As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients’ survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.
作为恶性肿瘤的并发症，脑转移瘤严重威胁患者的生存与生活质量。在制定放疗计划前准确检测脑转移瘤至关重要。由于脑转移瘤体积小、数量分布不均，其人工诊断面临巨大挑战。因此，基于磁共振成像的人工智能辅助脑转移瘤诊断具有重要意义。现有大多数基于深度学习的自动脑转移瘤检测方法试图在精确率与召回率之间取得平衡。然而受模型客观因素影响，较高的召回率往往伴随大量假阳性结果。在实际临床辅助诊断中，放射肿瘤科医生需要耗费大量精力复核这些假阳性病例。为在保持高准确率的同时降低假阳性率，本文提出一种改进的YOLOv5算法。首先，为聚焦特征图的重要通道，我们在网络颈部结构中引入卷积注意力模块。其次，新增专门检测小尺寸脑转移瘤的预测头。最后，为区分脑血管与小尺寸脑转移瘤，在最小预测头中嵌入Swin Transformer模块。通过引入F2分数指标确定最佳置信度阈值，本方法实现了0.612的精确率与0.904的召回率。与现有方法相比，本方法在减少假阳性结果方面表现出更优性能。预期该方法能够减轻放射肿瘤科医生在实际临床辅助诊断中的工作负担。
肿瘤(癌症)患者之家