Abstract
Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling the detection of intricate anomalies like intracranial hemorrhages, while the Transformer generates coherent, contextually relevant diagnostic reports by modeling long-range dependencies. We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset, where it outperforms traditional CNN-based models in both diagnostic accuracy and report generation. This solution not only supports radiologists in high-pressure environments but also provides a powerful educational tool for trainee physicians, offering real-time feedback and enhancing their learning experience. Our findings demonstrate the potential of combining advanced feature extraction with transformer-based text generation to improve clinical decision-making in the diagnosis of traumatic brain injuries.
Abstract (translated)
颅脑损伤在急诊医学中诊断难度大，及时解读医疗影像对于患者的预后至关重要。本文提出了一种基于人工智能的新型自动放射学报告生成方法，专门针对颅骨创伤病例设计。我们的模型结合了AC-BiFPN和Transformer架构，以捕获并处理复杂的医学成像数据，如CT和MRI扫描。其中，AC-BiFPN提取多尺度特征，能够检测到诸如脑内出血等细微异常情况；而Transformer通过建模长程依赖关系生成连贯且上下文相关的诊断报告。 我们在RSNA脑内出血检测数据集上评估了模型的性能，结果显示我们的方法在诊断准确性和报告生成方面都优于传统的基于CNN（卷积神经网络）的模型。这一解决方案不仅支持放射科医生在高压环境中工作，还为实习医师提供了一种强大的教育工具，能够实时反馈并提高其学习体验。 我们的研究结果展示了将高级特征提取与基于Transformer的文本生成相结合以改善颅脑损伤诊断中临床决策制定的潜力。
URL
https://arxiv.org/abs/2510.08498
