title: Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection.
abstract: OBJECTIVE
  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).
  
  
  CONCLUSIONS
  Our result suggests that the proposed adaptation technique is successful in reducing the divergence between TeUS RF and B-mode data.
authors.affiliation: The University of British Columbia, Vancouver, BC, Canada. shazizi@ece.ubc.ca.
authors.forename: Shekoofeh
authors.initials: S
authors.lastname: Azizi
authors.affiliation: Queen's University, Kingston, ON, Canada.
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.affiliation: Philips Research North America, Cambridge, MA, USA.
authors.forename: Pingkun
authors.initials: P
authors.lastname: Yan
authors.affiliation: Philips Research North America, Cambridge, MA, USA.
authors.forename: Amir
authors.initials: A
authors.lastname: Tahmasebi
authors.affiliation: Sejong University, Gwangjin-Gu, SU, Korea.
authors.forename: Jin Tae
authors.initials: JT
authors.lastname: Kwak
authors.affiliation: National Institutes of Health, Bethesda, MD, USA.
authors.forename: Sheng
authors.initials: S
authors.lastname: Xu
authors.affiliation: National Institutes of Health, Bethesda, MD, USA.
authors.forename: Baris
authors.initials: B
authors.lastname: Turkbey
authors.affiliation: National Institutes of Health, Bethesda, MD, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Choyke
authors.affiliation: National Institutes of Health, Bethesda, MD, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Pinto
authors.affiliation: National Institutes of Health, Bethesda, MD, USA.
authors.forename: Bradford
authors.initials: B
authors.lastname: Wood
authors.affiliation: The University of British Columbia, Vancouver, BC, Canada.
authors.forename: Purang
authors.initials: P
authors.lastname: Abolmaesumi
mesh_terms: D001707:Biopsy, Needle; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D011846:Radio Waves; D015203:Reproducibility of Results; D014463:Ultrasonography / Q000379:methods*
keywords: B-mode; Cancer diagnosis; Deep belief network; Deep learning; Prostate cancer; Radiofrequency signal; Temporal enhanced ultrasound; Transfer learning