Objective. Honeycomb lung is a rare but severe disease characterized by honeycomb-like
imaging features and distinct radiological characteristics. Therefore, this study aims to …

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role
in the diagnosis and treatment of various lung diseases. However, the availability of …

Honeycomb lung is a pulmonary manifestation that occurs in the terminal stage of various
lung diseases, which greatly threatens patients. Due to the different locations and irregular …

Honeycomb lung is a radiological manifestation of various lung diseases, seriously
threatening patients' lives worldwide. In clinical practice, the precise localization of lesions …

Due to the problems of more noise and lower contrast in X-ray tomography images of the
honeycomb lung, and the poor generalization of current medical segmentation algorithms …

Objective. Accurate and automated segmentation of lung tumors from computed tomography
(CT) images is critical yet challenging. Lung tumors are of various sizes and locations and …

Accurate lung parenchyma segmentation is vital for computer-aided lung cancer diagnosis.
Existing lung parenchyma segmentation networks excel at segmenting large and clear lung …

Objective. Deep learning networks such as convolutional neural networks (CNN) and
Transformer have shown excellent performance on the task of medical image segmentation …

Automatic and precise segmentation of lung images can assist doctors in locating and
diagnosing lung lesions. However, current traditional lung CT image lesion segmentation …

Computer-aided diagnosis based on deep learning is progressively deployed for the
analysis of medical images, yet poor robustness and generalization of the model pose a …