论文标题
指示与变压器胸部X光片多模式疾病分类的先验知识
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
论文作者
论文摘要
当临床医生将患者参考成像检查时，其中包括扫描请求中的原因（例如相关患者病史，可疑疾病）；这是放射学报告中的指示字段。图像的解释和报告在很大程度上受到此请求文本的影响，将放射科医生转向图像的特定方面。我们使用指示字段来驱动更好的图像分类，该网络通过在文本（BERT）上进行未段预先训练的变压器网络并进行微调以进行双模式分类。我们在模拟CXR数据集上评估了该方法，并进行了消融研究，以研究指示场对分类性能的影响。实验结果表明，我们的方法达到了87.8平均微极光，超过了单峰（84.4）和多模式（86.0）分类的最新方法。我们的代码可在https://github.com/jacenkow/mmbt上找到。
When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indication field in the radiology report. The interpretation and reporting of the image are substantially influenced by this request text, steering the radiologist to focus on particular aspects of the image. We use the indication field to drive better image classification, by taking a transformer network which is unimodally pre-trained on text (BERT) and fine-tuning it for multimodal classification of a dual image-text input. We evaluate the method on the MIMIC-CXR dataset, and present ablation studies to investigate the effect of the indication field on the classification performance. The experimental results show our approach achieves 87.8 average micro AUROC, outperforming the state-of-the-art methods for unimodal (84.4) and multimodal (86.0) classification. Our code is available at https://github.com/jacenkow/mmbt.
