A.1 ImplementationDetails Network Architecture: Inspired by [33], we utilize a pre-trained ResNet-50 [20] as the feature extractor for object recognition tasks (i.e., Office-31 [22], Office-Caltech [18] and Office-Home [46]). Theoverallframeworkis trained under an end-to-end manner via back-propagation. The stochastic gradient descent with momentum value as 0.9 is employed as the network optimizer. The initial learning rates for feature extractor and bottleneck layer are respectively set as 10 3 and 10 2, while the parameters of classifier are frozen. It is exponentially decayed as the training process.

Ab s tract -- 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 a nd Dice score of 0.8241 on the independent PTX - 498 dataset. These results demonstrate that the model can accurately localize pneumothoraces and support radiologists . Pneumothorax is the abnormal accumulation of air in the pleural space, which can arise spontaneously or due to trauma or medical procedures. Early detection is critical, as even small pneumothoraces may rapidly progress to life - threatening conditions. Clin ical examination alone may miss subtle cases [1], making chest X - rays the standard diagnostic tool.