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An Enhanced Region Proposal Network for object detection using deep learning method.

ABSTRACT: Faster Region-based Convolutional Network (Faster R-CNN) is a state-of-the-art object detection method. However, the object detection effect of Faster R-CNN is not good based on the Region Proposal Network (RPN). Inspired by RPN of Faster R-CNN, we propose a novel proposal generation method called Enhanced Region Proposal Network (ERPN). Four improvements are presented in ERPN. Firstly, our proposed deconvolutional feature pyramid network (DFPN) is introduced to improve the quality of region proposals. Secondly, novel anchor boxes are designed with interspersed scales and adaptive aspect ratios. Thereafter, the capability of object localization is increased. Thirdly, a particle swarm optimization (PSO) based support vector machine (SVM), termed PSO-SVM, is developed to distinguish the positive and negative anchor boxes. Fourthly, the classification part of multi-task loss function in RPN is improved. Consequently, the effect of classification loss is strengthened. In this study, our proposed ERPN is compared with five object detection methods on both PASCAL VOC and COCO data sets. For the VGG-16 model, our ERPN obtains 78.6% mAP on VOC 2007 data set, 74.4% mAP on VOC 2012 data set and 31.7% on COCO data set. The performance of ERPN is the best among the comparison object detection methods. Furthermore, the detection speed of ERPN is 5.8 fps. Additionally, ERPN obtains good effect on small object detection.

SUBMITTER: Chen YP

PROVIDER: S-EPMC6147513 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An Enhanced Region Proposal Network for object detection using deep learning method.

Chen Yu Peng YP Li Ying Y Wang Gang G

PloS one 20180920 9

Faster Region-based Convolutional Network (Faster R-CNN) is a state-of-the-art object detection method. However, the object detection effect of Faster R-CNN is not good based on the Region Proposal Network (RPN). Inspired by RPN of Faster R-CNN, we propose a novel proposal generation method called Enhanced Region Proposal Network (ERPN). Four improvements are presented in ERPN. Firstly, our proposed deconvolutional feature pyramid network (DFPN) is introduced to improve the quality of region pro ...[more]

PMID: 30235238

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Project description:Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where mammography studies are conducted more frequently; however, they are not yet globally employed for clinical use due to their inconsistent performance, which can be attributed to their reliance on hand-crafted features. It is difficult to use hand-crafted features for mammogram images that vary due to factors such as the breast density of patients and differences in imaging devices. To address these problems, several studies have leveraged a deep convolutional neural network that does not require hand-crafted features. Among the recent object detectors, RetinaNet is particularly promising as it is a simpler one-stage object detector that is fast and efficient while achieving state-of-the-art performance. RetinaNet has been proven to perform conventional object detection tasks but has not been tested on detecting masses in mammograms. Thus, we propose a mass detection model based on RetinaNet. To validate its performance in diverse use cases, we construct several experimental setups using the public dataset INbreast and the in-house dataset GURO. In addition to training and testing on the same dataset (i.e., training and testing on INbreast), we evaluate our mass detection model in setups using additional training data (i.e., training on INbreast + GURO and testing on INbreast). We also evaluate our model in setups using pre-trained weights (i.e., using weights pre-trained on GURO, training and testing on INbreast). In all the experiments, our mass detection model achieves comparable or better performance than more complex state-of-the-art models including the two-stage object detector. Also, the results show that using the weights pre-trained on datasets achieves similar performance as directly using datasets in the training phase. Therefore, we make our mass detection model's weights pre-trained on both GURO and INbreast publicly available. We expect that researchers who train RetinaNet on their in-house dataset for the mass detection task can use our pre-trained weights to leverage the features extracted from the datasets.

Dataset Information

An Enhanced Region Proposal Network for object detection using deep learning method.

Publications

An Enhanced Region Proposal Network for object detection using deep learning method.

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