Depth-Aware Networks for Multi-Organ Lesion Detection in Chest CT Scans.

ABSTRACT: Computer tomography (CT) scans' capabilities in detecting lesions have been increasing remarkably in the past decades. In this paper, we propose a multi-organ lesion detection (MOLD) approach to better address real-life chest-related clinical needs. MOLD is a challenging task, especially within a large, high resolution image volume, due to various types of background information interference and large differences in lesion sizes. Furthermore, the appearance similarity between lesions and other normal tissues demands more discriminative features. In order to overcome these challenges, we introduce depth-aware (DA) and skipped-layer hierarchical training (SHT) mechanisms with the novel Dense 3D context enhanced (Dense 3DCE) lesion detection model. The novel Dense 3DCE framework considers the

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11503988 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature