Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection
Open accessAbstract
Purpose We present a method for prostate cancer (PCa) detection using temporal enhanced ultrasound (TeUS) data obtained either from radiofrequency (RF) ultrasound signals or B-mode images.Methods For the first time, we demonstrate that by applying domain adaptation and transfer learning methods, a tissue classification model trained on TeUS RF data (source domain) can be deployed for classification using TeUS B-mode data alone (target domain), where both data are obtained on the same ultrasound scanner. This is a critical step for clinical translation of tissue classification techniques that primarily rely on accessing RF data, since this imaging modality is not readily available on all commercial scanners in clinics. Proof of concept is provided for in vivo characterization of PCa using TeUS B-mode data, where different nonlinear processing filters in the pipeline of the RF to B-mode conversion result in a distribution shift between the two domains.Results Our in vivo study includes data obtained in MRI-guided targeted procedure for prostate biopsy. We achieve comparable area under the curve using TeUS RF and B-mode data for medium to large cancer tumor sizes in biopsy cores (>4 mm).Conclusion Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Both pre-trained feature extraction and fine-tuning strategies achieve excellent performance in several specific tasks.
“Both strategies achieve excellent performance in several specific tasks [77,78].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [77]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247911 · Cited by 2,254 · see citing works
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.368 · Cited by 1,341 · see citing works
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 885 · see citing works
Epstein, J. I., Feng, Z., Trock, B. J., et al. (2012) · European Urology · doi:10.1016/j.eururo.2012.01.050 · Cited by 669 · see citing works
Supervised Representation Learning: Transfer Learning with Deep Autoencoders
Zhuang, F., Cheng, X., Luo, P., Pan, S. J., & He, Q. (2016) · Cited by 320 · see citing works
Oelze, M. L., O’Brien, W. D., Blue, J. P., et al. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.826953 · Cited by 257 · see citing works
Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols
van Opbroek, A., Ikram, M. A., Vernooij, M. W., & de Bruijne, M. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2366792 · Cited by 253 · see citing works
Inter-site and inter-scanner diffusion MRI data harmonization
Mirzaalian, H., Ning, L., Savadjiev, P., Pasternak, O., Sylvain Bouix, A., Michailovich, O., Grant, G. A., Marx, C. E., Morey, R. A., Flashman, L. A., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.041 · Cited by 153 · see citing works
Moradi, M., Abolmaesumi, P., Siemens, D. R., et al. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2009766 · Cited by 92 · see citing works
Self-citationWagner, M. E. H., Gellrich, N., Friese, K., Becker, M., Wolter, F.-E., Lichtenstein, J., Stoetzer, M., Rana, M., & Essig, H. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1228-8 · Cited by 80 · see citing works
A Novel Inference of a Restricted Boltzmann Machine
Tanaka, M., & Okutomi, M. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.271 · Cited by 77 · see citing works
Computer-Aided Diagnosis of Prostate Cancer with Emphasis on Ultrasound-Based Approaches: A Review
Moradi, M., Mousavi, P., & Abolmaesumi, P. (2007) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2007.01.008 · Cited by 62 · see citing works
Self-citationTissue typing using ultrasound RF time series: Experiments with animal tissue samples
Moradi, M., Abolmaesumi, P., & Mousavi, P. (2010) · Medical Physics · doi:10.1118/1.3457710 · Cited by 49 · see citing works
Self-citationComputer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study
Imani, F., Abolmaesumi, P., Gibson, E., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2427739 · Cited by 45 · see citing works
Self-citationAzizi, S., Imani, F., Ghavidel, S., Tahmasebi, A., Kwak, J. T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., et al. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1395-2 · Cited by 45 · see citing works
Self-citationvan Engelen, A., van Dijk, A. C., Truijman, M. T. B., et al. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2384733 · Cited by 34 · see citing works
Tissue Classification Using Ultrasound-Induced Variations in Acoustic Backscattering Features
Daoud, M. I., Mousavi, P., Imani, F., et al. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2224111 · Cited by 31 · see citing works
Self-citationAzizi, S., Imani, F., Zhuang, B., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_9 · Cited by 31 · see citing works
Self-citationRecent Advances in Ultrasonic Tissue-Type Imaging of the Prostate
Feleppa, E. J., Porter, C. R., Ketterling, J. A., et al. (2007) · Acoustical imaging · doi:10.1007/1-4020-5721-0_35 · Cited by 26 · see citing works
Conjeti, S., Katouzian, A., Roy, A. G., Peter, L., Sheet, D., CARLIER, S. G., Laine, A. F., & Navab, N. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.02.005 · Cited by 25 · see citing works
Ultrasound RF time series for tissue typing: firstin vivoclinical results
Moradi, M., Mahdavi, S. S., Nir, G., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2007672 · Cited by 23 · see citing works
Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis
Imani, F., Ramezani, M., Nouranian, S., et al. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2404300 · Cited by 18 · see citing works
RF Ultrasound Estimation from B-Mode Images
Seabra, J., & Sanches, J. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1180-2_1 · Cited by 11 · see citing works
Imani, F., Zhuang, B., Tahmasebi, A. M., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1184-3 · Cited by 11 · see citing works
Self-citationClassifying Cancer Grades Using Temporal Ultrasound for Transrectal Prostate Biopsy
Azizi, S., Imani, F., Kwak, J. T., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_76 · Cited by 10 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.009 · Cited by 93 · see citing works
- Authors
- Azizi, S., Mousavi, P., Yan, P., Tahmasebi, A., Kwak, J. T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., & Abolmaesumi, P.
- Affiliations
- University of British Columbia (CA)
- Queen's University (CA)
- Sejong University (KR)
- National Institutes of Health (US)
- Corresponding author
- Azizi, Shekoofeh
- ORCID
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2017-03-27
- Volume
- 12
- Issue
- 7
- Pages
- 1111-1121
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- PMC
- PMC8171585
- OpenAlex
- W2602001632
- Cited by
- 30
- References
- 31
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 7 · 2020: 6 · 2019: 5 · 2018: 3
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- prostate cancer detection, B-mode ultrasound, domain adaptation, transfer learning, tissue classification, distribution shift
- MeSH terms
- Biopsy, Needle, Humans, Magnetic Resonance Imaging, Male, Prostatic Neoplasms, Radio Waves, Ultrasonography, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
