Cascade Detector Analysis and Application to Biomedical Microscopy
This work addresses the need for faster inference in biomedical microscopy, offering incremental improvements in efficiency for domain-specific applications.
The paper tackled the problem of efficient inference for sparse object detection in multiresolution biomedical images by analyzing cascade detectors, showing that multi-level detectors achieve comparable performance with 30-75% less time in applications like fluorescent cell detection and organelle segmentation.
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.