Categorization of circulating tumor cells from lung cancer with compact deep learning
In person: 23 February 2022 • 11:10 AM - 11:30 AM PST
Cell characterization is key to research medical signaling of cancer-derived cells in the peripheral blood sample under the high-resolution fluorescent microscope. The task has been challenging with traditional image processing and machine learning techniques due to imaging artifacts, noise, debris, defocusing, shallow depth of field, and high variability in cell morphotypes and fluorescence. We present a compact deep learning method that combines the cell component segmentation/grouping with guided feature learning for categorizing circulating tumor cells from lung cancer liquid biopsy. The method demonstrates a promising performance with a small training dataset. It is effective, efficient, and valuable in low-cost clinical applications.
Union College (United States)