Domain adaptation from posteroanterior to anteroposterior X-ray radiograph classification via deep neural converter with label recycling

Dongwon Park, Minjun Kim, Hyungjin Kim, Jongho Lee, Se Young Chun

Abstract: Deep neural network trained with massive labeled dataset has shown impressive performance in multiple disease classification tasks for chest X-ray radiograph (CXR). However, different imaging protocols even for the same disease such as posteroanterior (PA) and anteroposterior (AP) CXR often lead to domain gap, causing significant performance degradation and requiring more images with painstaking labeling. We propose a novel domain adaptation scheme via deep neural converter (DNC) in the scenario of having labeled PA and unlabeled AP; converting labeled PA into AP with label recycling and pseudo-labeling unlabeled AP. To overcome no labeled AP data, our proposed method exploits DNC to convert labeled PA CXR into AP CXR with label recycling as well as pseudo labeler for unlabeled AP CXR.