IMPROVING LUNG NODULE DETECTION WITH LEARNABLE NON-MAXIMUM SUPPRESSION

Author(s)

Capia, Elvis R. ; Sousa, Azael M. ; Falcao, Alexandre X. ; IEEE

• Document type: Journal article
• Source: 2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020); v. N/A, p. 5-pg., 2020-01-01.

Abstract

Current lung nodule detection methods generate several candidate regions per nodule, such that a Non-Maximum Suppression (NMS) algorithm is required to select a single region per nodule while eliminating the redundant ones. GossipNet is a 1D Neural Network (NN) for NMS, which can learn the NMS parameters rather than relying on handcrafted ones. However, GossipNet does not take advantage of image features to learn NMS. We use Faster R-CNN with ResNet18 for candidate region detection and present FeatureNMS - a neural network that provides additional image features to the input of GossipNet, which result from a transformation over the voxel intensities of each candidate region in the CT image. Experiments indicate that FeatureNMS can improve nodule detection in 2.33% and 0.91%, on average, when compared to traditional NMS and the original GossipNet, respectively. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants