MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems

Abstract

This thesis presents an integrated framework for mechanical-guided, label-free cell sorting that synergizes high-speed localization with precise classification based on complementary feature modalities. The research addresses fundamental challenges in biomedical cell analysis through interconnected methodological innovations across detection, classification, and real-time processing domains.The cornerstone of this work is the development of Multiplex Image Machine Learning (MIML), a novel architecture that integrates bright-field microscopy images with cellular mechanical properties extracted during microfluidic transit. This hybrid approach achieves 98.3\% classification accuracy in distinguishing human colorectal carcinoma cells from white blood cells—representing an 8\% improvement over image-only methods. MIML demonstrates exceptional transfer learning capabilities, enabling effective classification of visually similar but mechanically distinct cells even with limited training datasets.To enable real-time applications, this research employs knowledge distillation techniques to compress a ResNet50-based teacher model into a student network with merely 0.02\% of the original parameters while maintaining robust classification performance. The subsequent FPGA implementation achieves unprecedented 14.5μs inference latency, establishing a new state-of-the-art benchmark that represents a 12-fold improvement over previous methods. Complementing this classification pipeline, a custom YOLO-based architecture optimized for high-speed microfluidic videos provides real-time cell detection and tracking. This detection framework integrates with Kalman filtering for robust trajectory analysis and extracts on-the-fly mechanical descriptors including deformation indices, velocity profiles, and transition times.By unifying these approaches, the thesis demonstrates a comprehensive system that advances label-free cell classification while maintaining high specificity, minimizing computational requirements, and operating at sub-millisecond latencies suitable for real-time applications. The integrated platform has significant implications for clinical diagnostics, cancer detection, and personalized therapies where label-free, high-throughput cell analysis is essential. This work establishes a cohesive narrative bridging high-speed cell handling with multi-modal data analysis, addressing critical challenges in microscopy-based cell classification and setting the foundation for future breakthroughs in biomedical engineering and cellular diagnostics.</p