I am a lecturer at the University of Haifa in the Medical Imaging Sciences department and a Postdoc at the Martinos Center for Biomedical Engineering. I earned my PhD from the Hebrew University. Previously, I worked at Philips as a scientist in the Medical Imaging domain.

My main research interests are: 1) Reliable medical imaging algorithms for safe use in healthcare. 2) Affordable and easy to use medical imaging algorithms. 3) Medical imaging tools based on clinical needs (e.g Fetal MRI).

Google Scholar account: https://scholar.google.com/citations?hl=en&user=SFoiA_wAAAAJ

Refereed journal papers

  1. Specktor Fadida B., Link-Sourani D., Rabinovich A., Miller E., Levchakov A., Avisdris N., Ben Sira L., Hierch L., Joskowicz L and Ben Bashat D. Deep Learning-based Segmentation of Whole-Body Fetal MRI and Fetal Weight Estimation: Assessing Performance, Repeatability and Reproducibility. European Radiology. 2023 Sep 2:1-2.
  2. Rabinowich A, Avisdris N, Zilberman A, Link-Sourani D, Lazar S, Herzlich J, Specktor-Fadida B, Joskowicz L, Malinger G, Ben-Sira L, Hiersch L, Ben-Bashat D. Reduced adipose tissue in growth-restricted fetuses using quantitative analysis of magnetic resonance images. European Radiology. 2023 Jun 30:1-9.
  3. K. Payette, ... , Specktor-Fadida B. and 25 authors. Fetal brain tissue annotation and segmentation challenge results. Medical Image Analysis. 2023 April 22:102833.
  4. Fadida-Specktor B. Preprocessing Prediction of Advanced Algorithms for Medical Imaging. Journal of Digital Imaging. 2018 Feb;31(1):42-50.

Refereed conference papers

  1. Specktor-Fadida B, Levchakov A, Shonberger D, Ben-Sira L, Ben-Bashat D and Joskowicz L. Test-time augmentation-based active learning and self-training. Accepted as an oral presentation to MICCAI workshop on Medical Image Learning with Limited and Noisy Data, 2023; proceedings TBD
  2. Specktor Fadida B., Yehuda B., Link Sourani D., Ben Sira L., Ben Bashat D., Joskowicz L. Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI. In: Computer Vision–ECCV 2022 Workshops: October 23–27, 2022, Proceedings, Part III 2023 Feb 18 (pp. 355-368).
  3. Specktor Fadida B., Link Sourani D., Ben Sira L., Miller E., Ben Bashat D., Joskowicz L. Partial annotations for the segmentation of large structures with low annotation cost. In: Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, September 22, 2022, Proceedings 2022 Sep 15 (pp. 13-22).
  4. Specktor-Fadida B, Link-Sourani D, Ferster-Kveller S, Ben-Sira L, Miller E, Ben-Bashat D, Joskowicz L. A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI. InUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis 2021 Oct 1 (pp. 189-199). Springer, Cham.
  5. Osadchy M, Keren D, Fadida-Specktor B. Hybrid classifiers for object classification with a rich background. In: European Conference on Computer Vision 2012 Oct 7 (pp. 284-297). Springer, Berlin, Heidelberg.

Refereed abstracts

  1. Specktor-Fadida B, Link Sourani D, Ben-Sira L, Ben Bashat D, Joskowicz L. Slices prioritization for segmentation correction in fetal body MRI scans based on previous corrections data. CARS 2022
  2. Fadida-Specktor B, Ben-Bashat D, Link-Sourani D, Avisdris N, Miller E, Ben-Sira L and Joskowicz L. Automatic Segmentation and Normal Dataset of Fetal Body from Magnetic Resonance Imaging. ISMRM 2021.

Invited Talks

Conference Talks

Additional Talks

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