Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation.
Cardiovascular disease (CVD) is the number one killer in the USA, yet it is largely preventable (World Health Organization 2011). To prevent CVD, carotid intima-media thickness (CIMT) imaging, a noninvasive ultrasonography method, has proven to be clinically valuable in identifying at-risk persons b...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 2; pp. 290 - 300 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135821486&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135821486 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2019 vid: 32 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135821486 135821486 135821486 10.1007/s10278-018-0143-2 135821486 ppf: 290 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation. aug: au: Zhou, Zongwei Shin, Jae Feng, Ruibin Hurst, R. Todd Kendall, Christopher B. Liang, Jianming affil: Arizona State University, 13212 E Shea Blvd, 85259, Scottsdale, AZ, USA sug: subj: Cardiovascular Diseases Diagnosis Risk Assessment Methods Carotid Intima-Media Thickness Methods Radiographic Image Interpretation, Computer-Assisted Education Human Carotid Intima-Media Thickness Economics Radiographic Image Interpretation, Computer-Assisted Methods Image Processing, Computer Assisted Methods Algorithms Utilization Machine Learning Methods ab: Cardiovascular disease (CVD) is the number one killer in the USA, yet it is largely preventable (World Health Organization 2011). To prevent CVD, carotid intima-media thickness (CIMT) imaging, a noninvasive ultrasonography method, has proven to be clinically valuable in identifying at-risk persons before adverse events. Researchers are developing systems to automate CIMT video interpretation based on deep learning, but such efforts are impeded by the lack of large annotated CIMT video datasets. CIMT video annotation is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible. To dramatically reduce the cost of CIMT video annotation, this paper makes three main contributions. Our first contribution is a new concept, called Annotation Unit (AU), which simplifies the entire CIMT video annotation process down to six simple mouse clicks. Our second contribution is a new algorithm, called AFT (active fine-tuning), which naturally integrates active learning and transfer learning (fine-tuning) into a single framework. AFT starts directly with a pre-trained convolutional neural network (CNN), focuses on selecting the most informative and representative AU s from the unannotated pool for annotation, and then fine-tunes the CNN by incorporating newly annotated AU s in each iteration to enhance the CNN's performance gradually. Our third contribution is a systematic evaluation, which shows that, in comparison with the state-of-the-art method (Tajbakhsh et al., IEEE Trans Med Imaging 35(5):1299–1312, 2016), our method can cut the annotation cost by >81% relative to their training from scratch and >50% relative to their random selection. This performance is attributed to the several advantages derived from the advanced active, continuous learning capability of our AFT method. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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