# Bounding Box Priors for Cell Detection with Point Annotations

arXiv: https://arxiv.org/abs/2211.06104 · HTML: https://mlsanity.com/paper/2211.06104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Points as Queries: Weakly Semi-supervised Object Detection by Points](https://mlsanity.com/paper/2104.07434/) (2021)
- [Group R-CNN for Weakly Semi-supervised Object Detection with Points](https://mlsanity.com/paper/2205.05920/) (2022)

## Built on by (most cited first, 1 of 1)

- [UAVDB: Point-Guided Masks for UAV Detection and Segmentation](https://mlsanity.com/paper/2409.06490/) (2024)

## Abstract (arXiv)

The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary?" before training a machine learning model.