Categorization of tumor-derived cells from lung cancer with compact deep learning

Cell characterization is key to research medical signaling of cancer-derived cells in the peripheral blood sample under the high-resolution fluorescence microscope. The task has been challenging with traditional image processing and machine learning techniques due to imaging artifacts, noise, debris, de-focusing, shallow depth of field, and high variability in cell morphotypes and fluorescence. We present a compact deep learning method that combines cell component segmentation and grouping with a 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. Characterization of cancer-derived cells could provide vital insights into cancer metastasis and contribute to development of novel targeted therapies.