Cascade Detector Analysis and Application to Biomedical Microscopy
Journal:
arXiv
Published Date:
Apr 30, 2025
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.
