用级联检测器加速生物显微图像中稀疏目标的识别。
Cascade Detector Analysis and Application to Biomedical Microscopy
- 设计多层级级联检测框架，按分辨率逐层筛选目标区域。
- 在细胞、细胞器和组织分割任务中，速度提升30%至75%。
- 适用于多种视觉模型与显微成像数据，通用性强。
随着计算机视觉模型和生物医学数据集规模增大，高效推理算法的需求日益迫切。本文利用级联检测器，在多分辨率图像中高效识别稀疏物体。基于目标出现频率及各分辨率下已知准确率的检测器，推导出级联检测器的准确率和预期分类器调用次数，结果可推广至不同维度和级联层级。最后，在荧光细胞检测、细胞器分割和组织分割任务中，对比了一级与二级检测器在多种显微成像模态下的表现。结果表明，多层级检测器在保持相当性能的前提下，耗时减少30%-75%。本方法兼容多种计算机视觉模型与数据领域。
原文摘要 · Abstract (English)
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.
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