# CircleNet: Anchor-free Detection with Circle Representation

arXiv: https://arxiv.org/abs/2006.02474 · HTML: https://mlsanity.com/paper/2006.02474/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology](https://mlsanity.com/paper/2411.16961/) (2024)
- [Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining](https://mlsanity.com/paper/2206.00123/) (2022)
- [TransGOP: Transformer-Based Gaze Object Prediction](https://mlsanity.com/paper/2402.13578/) (2024)
- [EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography](https://mlsanity.com/paper/2109.12474/) (2021)
- [Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation](https://mlsanity.com/paper/2101.07654/) (2021)
- [CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection](https://mlsanity.com/paper/2206.00338/) (2022)
- [Segmentation-free Direct Iris Localization Networks](https://mlsanity.com/paper/2210.10403/) (2022)
- [CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention](https://mlsanity.com/paper/2308.16145/) (2023)
- [Few-shot Shape Recognition by Learning Deep Shape-aware Features](https://mlsanity.com/paper/2312.01315/) (2023)
- [Automated Pruning of Polyculture Plants](https://mlsanity.com/paper/2208.10472/) (2022)
- [Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results](https://mlsanity.com/paper/2406.19540/) (2024)
- [Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification](https://mlsanity.com/paper/2202.00087/) (2022)
- [CRRS: Concentric Rectangles Regression Strategy for Multi-point Representation on Fisheye Images](https://mlsanity.com/paper/2303.14639/) (2023)
- [An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration](https://mlsanity.com/paper/2305.11968/) (2023)
- [Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation](https://mlsanity.com/paper/2411.15942/) (2024)

## Abstract (arXiv)

Object detection networks are powerful in computer vision, but not necessarily optimized for biomedical object detection. In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. Different from the traditional bounding box based detection method, the bounding circle (1) reduces the degrees of freedom of detection representation, (2) is naturally rotation invariant, (3) and optimized for ball-shaped objects. The key innovation to enable this representation is the anchor-free framework with the circle detection head. We evaluate CircleNet in the context of detection of glomerulus. CircleNet increases average precision of the glomerulus detection from 0.598 to 0.647. Another key advantage is that CircleNet achieves better rotation consistency compared with bounding box representations.