Automated mammographic mass detection using deformable convolution and multiscale features.
Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection C...
| Published in: | Medical & Biological Engineering & Computing Vol. 58; no. 7; pp. 1405 - 1418 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jul2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143819687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143819687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2020 vid: 58 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143819687 143819687 143994944 NLM32297129 10.1007/s11517-020-02170-4 NLM32297129 143819687 ppf: 1405 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Automated mammographic mass detection using deformable convolution and multiscale features. aug: au: Peng, Junchuan Bao, Changyu Hu, Chuting Wang, Xianming Jian, Wenjing Liu, Weixiang affil: School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, Shenzhen, Guangdong, People's Republic of China sug: subj: Mammography Methods Image Processing, Computer Assisted Methods Breast Neoplasms Diagnosis, Computer Assisted Methods Resource Databases Female Female ab: Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|