| Popis: |
Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Variousdeep learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are notreliable to be used by medical practitioners. Specifically, these approaches do not exploit the complex texture patternsand interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalabilityof these methods under insufficient labelled datasets. Further, these models lack generalisation capability to newsynthesizedpatterns/textures. To address these problems, in the first instance, we design a graph model to transform themammograms images into highly correlated multigraph encode rich structural relations and high-level texture features.Then, we consider a self-supervised learning multigraph encoder (SSL-MG) to improve the features presentation,especially under limited labelled data constraints. Finally, we design a semi-supervised mammogram multigraphconvolution neural network downstream model (MMGCN) to perform multi classifications of mammogram segmentsencoded in the multigraph nodes. We evaluate the classification performance of MMGCN independently and withintegration with SSL-MG in a model called SSL-MMGCN over multi training settings. Our results reveal the efficientlearning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast tothe multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy. |
