Daniel Luci

Methods of Squamous Cell Segmentation

Using images acquired by Alexapath's mobile Whole Slide Imaging (mWSI) process, Mr. Luci is developing an AI capable of diagnosis Pap smears with a 99% sensitivity. Set to begin clinical trials this fall in India, Mr. Luci will discuss his methods for improving diagnostic accuracy.

Daniel Luci graduated from NYU Tandon with a masters in Chemical Engineering, during school his research consisted of writing a program to interpret temperature data from a video feed of the chemical reactions in a continuous flow. High throughput testing of catalyst efficiency can be achieved in this way. He joined Alexapath earlier this year as the lead computer vision / machine learning researcher. Daniel is focused on segmentation of images of cells acquired through Alexapath's mobile Whole Slide Imaging process. His mission is to develop a low cost auto diagnostic for cervical cancer capable of running on Qualcomm's SnapDragon 820/821 chip.

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