Deep learning models for bone suppression in chest radiographs
Source of Publication
2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
© 2017 IEEE. Bone suppression in lung radiographs is an important task, as it improves the results on other related tasks, such as nodule detection or pathologies classification. In this paper, we propose two architectures that suppress bones in radiographs by treating them as noise. In the proposed methods, we create end-to-end learning frameworks that minimize noise in the images while maintaining sharpness and detail in them. Our results show that our proposed noise-cancellation scheme is robust and does not introduce artifacts into the images.
Gusarev, Maxim; Kuleev, Ramil; Khan, Adil; Rivera, Adin Ramirez; and Khattak, Asad Masood, "Deep learning models for bone suppression in chest radiographs" (2017). Scopus Indexed Articles. 1258.