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Dense networks with relative location awareness for thorax disease identification
Author(s) -
Liang Xiao,
Peng Chengtao,
Qiu Bensheng,
Li Bin
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13516
Subject(s) - computer science , thorax (insect anatomy) , identification (biology) , artificial intelligence , pattern recognition (psychology) , euclidean distance , medical imaging , computer vision , medicine , botany , anatomy , biology
Purpose Chest X‐ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X‐ray images have been proposed. To our knowledge, almost none of the previous auto‐diagnosis algorithms consider the effect of relative location information on disease incidence. In this study, we propose to use relative location information to assist the identification of thorax diseases. Method In this work, U‐N et is used to segment lung and heart from chest image. The relative location maps are computed through Euclidean distance transformation from segmented masks. By introducing the relative location information into the network, the usual location of disease is combined with the incidence. The proposed network is the fusion of two branches: mask branch and image branch. A mask branch is designed to be a bottom‐up and top‐down structure to extract relative location information. The structure has a large receptive field, which can extract more information for large lesion and contextual information for small lesion. The features learned from mask branch are fused with image branch, which is a 121‐layers DenseNet. Results We compare our proposed method with four state‐of‐the‐art methods on the largest public chest X‐ray dataset: ChestX‐ray14. The proposed method achieves the area under a curve of 0.820, which outperforms all the existing models and algorithms. Conclusion This paper proposed a dense network with relative location information to identify thorax disease. The method combines the usual location of disease with the incidence for the first time and performs good.