A novel attention fusion network-based framework to ensemble the predictions of CNNs for lymph node metastasis detection

Diagnosis of lymph node metastases is a challenging task for pathologists, involving an extensive screening of the pathological scans. Automating diagnostic processes reduces the workload of pathologists and yields high accuracy by the virtue of advances in technology. In this study, a novel ensemble-based framework is proposed for the classification of lymph node metastases. The proposed ensemble framework comprises different pre-trained CNN models such as DenseNet201, InceptionV3 and ResNeXt-50. In the proposed framework, an attention fusion network is utilized to amalgamate the predictions of the individual models. The proposed framework achieves an AUC-ROC of 0.9816 which surpasses the highest AUC-ROC achieved by the conventional approaches on the PCam benchmark dataset.