Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network

Lymphatic metastasis is one of the most common proliferation pathways of thyroid carcinoma. Accurate diagnosis of lymph nodes is of great significance to surgical planning and prognosis. Due to the continuous development of deep learning recently, computer-aided diagnosis (CAD) systems for thyroid cancer have made considerable progress, but the research on the effective diagnosis of lymphatic metastasis remains insufficient. Focusing on this issue, we propose a semi-supervised multi-view network to diagnose lymph node metastasis, which combines coarse-view and fine-view to obtain a more comprehensive description. This method consists of three parts as follows: 1) joint probabilistic labels of the nodule partition information are generated by fuzzy clustering and perform semi-supervised learning on coarse-view with real labels; 2) an attention mechanism based network for fine-view is designed to capture various differentiated local features in a pyramid manner; 3) the two parts are then combined to extract global and local features more effectively to derive more accurate diagnostic reasoning. Especially, the introduction of fuzzy logic greatly reduces the impact of the uncertainty of the generated labels, thereby ensuring the effectiveness of the pseudo-labels. Extensive experiments on our collected dataset demonstrate that the proposed method is more efficient than other state-of-the-art methods.