Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning
Journal:
arXiv
Published Date:
Mar 16, 2025
Abstract
Precise cell classification is essential in biomedical diagnostics and
therapeutic monitoring, particularly for identifying diverse cell types
involved in various diseases. Traditional cell classification methods such as
flow cytometry depend on molecular labeling which is often costly,
time-intensive, and can alter cell integrity. To overcome these limitations, we
present a label-free machine learning framework for cell classification,
designed for real-time sorting applications using bright-field microscopy
images. This approach leverages a teacher-student model architecture enhanced
by knowledge distillation, achieving high efficiency and scalability across
different cell types. Demonstrated through a use case of classifying lymphocyte
subsets, our framework accurately classifies T4, T8, and B cell types with a
dataset of 80,000 preprocessed images, accessible via an open-source Python
package for easy adaptation. Our teacher model attained 98\% accuracy in
differentiating T4 cells from B cells and 93\% accuracy in zero-shot
classification between T8 and B cells. Remarkably, our student model operates
with only 0.02\% of the teacher model's parameters, enabling field-programmable
gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an
ultra-low inference latency of just 14.5~$\mu$s and a complete cell
detection-to-sorting trigger time of 24.7~$\mu$s, delivering 12x and 40x
improvements over the previous state-of-the-art real-time cell analysis
algorithm in inference and total latency, respectively, while preserving
accuracy comparable to the teacher model. This framework provides a scalable,
cost-effective solution for lymphocyte classification, as well as a new SOTA
real-time cell sorting implementation for rapid identification of subsets using
in situ deep learning on off-the-shelf computing hardware.
