BIO:
Assaf Spanier is a highly experienced algorithm developer and software programmer, specializing in medical image processing and computer vision, deep learning and machine learning. Assaf took part in numerous projects and research and development projects, both, in the industry and academia, with emphasis on medical image processing algorithms, statistical inference and machine and deep learning.
Assaf Hold a B.Sc. in Computer Science from Hadassah College, Jerusalem, And an ME in Biomedical Engineering from the Technion. In his Master’s thesis Assaf developed a classification method based on EEG signals to distinguish healthy patients, in order to distinguish the treatment effect. Assaf received a Ph.D. from the Hebrew University of Jerusalem in 2017. For his PhD thesis Assaf developed an end-to-end system for image retrieval. Assaf's PhD title was "Structure-Specific Automatic Multi-Parametric Medical Image Analysis and Retrieval"
Research interests:
medical image processing and computer vision, deep learning and machine learning.
Representative Publications:
- Spanier AB, Cohen D, Joskowicz L (2017) A new method for the automatic retrieval of medical cases based on the RadLex ontology The International Journal of Computer Assisted Radiology and Surgery (IJCARS) 12, pp.471–484.
- Spanier AB, Caplan N, Sosna J, Acar B, Joskowicz L (2018) A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations. The International Journal of Computer Assisted Radiology and Surgery (IJCARS) 15 pp.1–10.
- Zilkha, M., Spanier, A. B. (2019). Real-time CNN-based object detection and classification for outdoor surveillance images: daytime and thermal. In Artificial Intelligence and Machine Learning in Defense Applications (Vol. 11169, p. 1116902). International Society for Optics and Photonics. [Invited paper[
- Exman, I., Spanier, A. B. (2019). Algebraic Convergence to Software-Knowledge: Deep Software Learning (TSE).
- Turner, A. Spanier, A. B., (2019). LSTM in VQA-Med, is It Really Needed? JCE Study on the ImageCLEF 2019 Dataset. In CLEF (Working Notes).
- Tobias G, Spanier AB (2020) Developing a Mobile App (iGAM) to Promote Gingival Health by Professional Monitoring of Dental Selfies: User-Centered Design Approach JMIR mHealth and uHealth 8 (8), e19433
