Prof. Bahram Jalali presented a keynote talk entitled “Deep Learning Microscope” at the The 7th International Multidisciplinary Conference on Optofluidics 2017 in Singapore. The talk described our group’s success in detection of cancer cells in blood...
Time stretch can be readily analysed by modelling the band-pass optical signals with low-pass complex envelopes. The transfer function of the dispersive element in the time-stretch system, H(ω), is mainly a phase propagator: in which ϕ(ω) is the phase profile of the...
Professor Jalali and his students Ata Mahjoubfar and Claire Chen have published a book that details the world’s fastest and most accurate AI-powered microscope for identification of biological cells. Used with a microfluidics chip, the microscope has proven...
Our technical article published by Mathworks® highlights that the TS-QPI system generates equivalent of 20 HD movies per second. For a single experiment, in which every cell in a 10-milliliter blood sample is imaged, the system generates from 10 to 50 terabytes of...
Mathworks® has featured our recently published research which combines microscopy and deep learning for cancer diagnosis. The article explains how Matlab® can be used for design, simulation, and modeling of quantitative phase imaging, amplified time-stretch, and big...
The time-stretch microscopy technique which was first developed in the Jalali lab has recently been combined with artificial intelligence for cancer cell detection. This research work was enthusiastically received in the scientific community and has been covered by 70...
