Abstract
Pneumothorax, the abnormal accumulation of air in the pleural space, can be life-threatening if undetected. Chest X-rays are the first-line diagnostic tool, but small cases may be subtle. We propose an automated deep-learning pipeline using a U-Net with an EfficientNet-B4 encoder to segment pneumothorax regions. Trained on the SIIM-ACR dataset with data augmentation and a combined binary cross-entropy plus Dice loss, the model achieved an IoU of 0.7008 and Dice score of 0.8241 on the independent PTX-498 dataset. These results demonstrate that the model can accurately localize pneumothoraces and support radiologists.
Abstract (translated)
气胸是指肺部周围积聚了异常的空气，这种情况如果未被发现可能会危及生命。胸部 X 光片是首选诊断工具，但较小的情况可能难以察觉。我们提出了一种使用 U-Net 结构和 EfficientNet-B4 编码器的自动化深度学习流程，用于分割气胸区域。该模型在 SIIM-ACR 数据集上进行训练，并采用了数据增强及结合二元交叉熵与 Dice 损失函数的组合方法。经过训练后，该模型在独立的 PTX-498 数据集中达到了 0.7008 的交并比（IoU）和 0.8241 的 Dice 分数。这些结果表明，该模型能够准确地定位气胸，并且可以支持放射科医生的工作。
URL
https://arxiv.org/abs/2509.03950
