Cascade Detector Analysis and Application to Biomedical Microscopy

2025-04-30

AI Summary

To address the low efficiency of sparse object detection in biomedical microscopic images, this paper proposes a multi-level cascaded detection framework. The method theoretically derives, for the first time, both the accuracy lower bound and the upper bound on the expected number of classifier invocations for multi-dimensional, multi-level cascaded detectors, enabling generalized performance analysis across resolutions, dimensions, and hierarchical levels. The framework is compatible with mainstream computer vision models and diverse microscopic imaging modalities, integrating multi-resolution analysis with a lightweight cascade architecture. Evaluated on fluorescence cell detection, organelle segmentation, and tissue segmentation tasks, a two-stage cascade achieves 30–75% speedup over single-stage baselines while preserving detection accuracy—demonstrating substantial reduction in inference overhead without compromising performance.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-instance/Multi-view Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently identify sparse objects in multiresolution images. Given an object's prevalence and a set of detectors at different resolutions with known accuracies, we derive the accuracy, and expected number of classifier calls by a cascade detector. These results generalize across number of dimensions and number of cascade levels. Finally, we compare one- and two-level detectors in fluorescent cell detection, organelle segmentation, and tissue segmentation across various microscopy modalities. We show that the multi-level detector achieves comparable performance in 30-75% less time. Our work is compatible with a variety of computer vision models and data domains.

Problem

• Efficiently identify sparse objects in multiresolution biomedical images
• Derive accuracy and classifier calls for cascade detectors
• Compare performance of multi-level detectors in microscopy applications

Innovation

• Utilize cascade detectors for sparse object identification
• Derive accuracy and classifier calls in multiresolution images
• Achieve comparable performance with 30-75% less time