Affiliation:
1. Beijing Lab of Intelligent Information, School of Computer Science, Beijing Institute of Technology, Beijing, China
Abstract
We present a 3D deep neural network known as URDNet for detecting ground-glass opacity (GGO) nodules in 3D CT images. Prior work on GGO detection repurposes classifiers on a large number of windows to perform detection or fine-tuning by box regression based on a previous window classification step. Instead, we consider GGO detection as a multitarget regression problem to focus on the location of GGO. Furthermore, to capture multiscale information, we introduce a backbone network which is a contracting-expanding structure similar to 2D U-net, but we inject the source CT inputs into each layer in the contracting pathway to prevent source information loss at different scales. At last, we propose a two-stage training method for URDNet. In the first stage, the backbone of the network for feature extraction is trained, and in the second, the overall URDNet is fine-tuned based on the previous pretrained weights. By using this training method in conjunction with data augmentation and hard negative mining techniques, our URDNet can be effectively trained even on a small amount of annotated CT images. We evaluate the proposed method on the LIDC-IDRI dataset. It achieves the sensitivity of 90.8% with only 1 false positive per scan. Experimental results show that our detection method achieves the superior detection performance over the state-of-the-art methods. Due to its simplicity and effective, URDNet can be easier to apply to medical IoT systems for improving the efficiency of overall health systems.
Funder
Program for New Century Excellent Talents in University
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
2 articles.
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