Our recent work “Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry” which shows that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time stretch quantitative phase imaging, microfluidics and deep learning is published in Scientific Reports.
Recent Posts
- Jalali-Lab Open-sources PhyCV: The First Physics-inspired Computer Vision Library
- Jalali-Lab Open-Sources Code for Physics-Inspired Algorithm for Feature Extraction in Digital Images
- Professor Bahram Jalali Elected to the National Academy of Engineering
- Celebrating a Successful “AI and Optical Data Science Conference” at Photonics West 2022 Jalali-Lab
- Celebrating Jalali-Lab’s 30-year anniversary in Silicon Photonics
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