Contrastive Swin Transformer with Masked Autoencoder for Pancreatic Cancer Computed Tomography Image Classification in the Internet of Medical Things

Pancreatic cancer is one of the most aggressive malignant solid tumors, and achieving early screening and diagnosis is the key to improving patient survival rates. Although deep learning has made significant progress in medical image analysis, the classification of pancreatic cancer computed tomography (CT) images remains highly challenging due to subtle inter-lesion differences and overlapping category distributions. Moreover, existing methods rely heavily on large amounts of high-quality annotations, which can lead to overfitting and hinder the effective exploitation of structural and semantic features within images. To address these challenges, we propose a Contrastive Swin Transformer with Masked Autoencoder (CSTMA) for pancreatic cancer CT image classification in the Internet of Medical Things (IoMT) environment. CSTMA leverages contrastive learning to enhance feature discriminability, while its multi-task self-supervised architecture based on Masked Autoencoder guides the model to learn both structural and semantic representations. We conduct comprehensive experiments on pancreatic cancer CT image classification tasks, and the results demonstrate that the proposed CSTMA model achieves superior performance across multiple evaluation metrics.