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

Yongjian Yu; Jue Wang

doi:10.1117/12.2611189

4 April 2022 Categorization of tumor-derived cells from lung cancer with compact deep learning

Yongjian Yu, Jue Wang

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203310 (2022) https://doi.org/10.1117/12.2611189
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Yongjian Yu and Jue Wang "Categorization of tumor-derived cells from lung cancer with compact deep learning", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203310 (4 April 2022); https://doi.org/10.1117/12.2611189

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