Deep Learning for Breast MRI Style Transfer with Limited Training Data.
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the traini...
| Published in: | Journal of Digital Imaging Vol. 36; no. 2; pp. 666 - 679 |
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| Main Authors: | , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162679425&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162679425 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2023 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162679425 160902835 162679425 162679425 10.1007/s10278-022-00755-z 162679425 ppf: 666 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning for Breast MRI Style Transfer with Limited Training Data. aug: au: Cao, Shixing Konz, Nicholas Duncan, James Mazurowski, Maciej A. affil: Department of Electrical and Computer Engineering, Duke University, 27704, Durham, NC, USA sug: subj: Deep Learning Magnetic Resonance Imaging Breast Neoplasms Radiography Machine Learning Diagnostic Imaging Methods Image Processing, Computer Assisted Methods Image Interpretation, Computer Assisted Image Retrieval Computer Simulation Image Enhancement Scanners Electronic Data Interchange Health Information Management Computer Communication Networks Utilization ab: In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Our model disentangles image content from style and can modify an image's style by simply replacing the style encoding with one extracted from a single image of the target style, with no additional optimization required. This also allows the model to distinguish between different styles of images, including among those that were unseen in training. We propose a formal description of the proposed model. Experimental results on breast magnetic resonance images indicate the effectiveness of our method for style transfer. Our style transfer method allows for the alignment of medical images taken with different scanners into a single unified style dataset, allowing for the training of other downstream tasks on such a dataset for tasks such as classification, object detection and others. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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