Paper Info
Title
Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information.
Abstract
Detecting different kinds of lesions in computed tomography (CT) scans at the same time is a difficult but important task for a computer-aided diagnosis (CADx) system. Compared to single-lesion detection methods, our lesion detection method considers additional intra-class differences. In this work, we present a CT image analysis framework for lesion detection. Our model is developed based on a dense region-based fully convolutional network (Dense R-FCN) model using 3D context and is equipped with a dense auxiliary loss (DAL) scheme for end-to-end learning. It fuses shallow, medium, and deep features to meet the needs of detecting lesions of various sizes. Owing to its fully-connected structure, it is called Dense R-FCN. Meanwhile, the DAL supervises the intermediate hidden layers in order to maximize the use of the shallow layer information, which benefits the detection results, especially for small lesions. Experiment results on the DeepLesion dataset corroborate the efficacy of our method.
Year
DOI
Venue
2019
10.1007/978-3-030-32692-0_6
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
11861
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Han Zhang124315.29
Albert C. S. Chung296472.07