ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
CircleNet: Anchor-free Glomerulus Detection with Circle Representation.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Glo-In-One-v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Vispro improves imaging analysis for Visium spatial transcriptomics.Genome biology · 2025Article
- Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
- A semi-supervised learning framework for micropapillary adenocarcinoma detection.International journal of computer assisted radiology and surgery · 2022Article
- Circle Representation for Medical Object Detection.IEEE transactions on medical imaging · 2022Article
- Holistic fine-grained global glomerulosclerosis characterization: from detection to unbalanced classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images.IEEE transactions on medical imaging · 2021Article
- AI applications in renal pathology.Kidney international · 2021Review
- Real-Time Instance Segmentation of Traffic Videos for Embedded Devices.Sensors (Basel, Switzerland) · 2021Article
- Dense Multi-Object 3D Glomerular Reconstruction and Quantification on 2D Serial Section Whole Slide Images.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
10 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.