ArticleProceedings. IEEE International Conference on Computer Vision2021
Multi-Class Cell Detection Using Spatial Context Representation.
Article in Proceedings. IEEE International Conference on Computer Vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Optimization of deep learning models for inference in low resource environments.Computers in biology and medicine · 2025Article
- Article
- Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology.Scientific reports · 2025Article
- A Topological Comparison of the Fluorescence Imitating Brightfield Imaging and H&E Imaging.Topology- and graph-informed imaging informatics : first International Workshop, TGI3 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. International Workshop on Topology- and Graph-Informed ... · 2025Article
- New Spatial Phenotypes from Imaging Uncover Survival Differences for Breast Cancer Patients.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024Article
- Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes.American journal of reproductive immunology (New York, N.Y. : 1989) · 2024Article
- Spatial Diffusion for Cell Layout Generation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Leukotriene B4 receptor 1 (BLT1) does not mediate disease progression in a mouse model of liver fibrosis.The Biochemical journal · 2023Article
- Topology-Guided Multi-Class Cell Context Generation for Digital Pathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in practice pathologists often infer cell classes through their spatial context. In this paper, we propose a novel method for both detection and classification that explicitly incorporates spatial contextual information. We use the spatial statistical function to describe local density in both a multi-class and a multi-scale manner. Through representation learning and deep clustering techniques, we learn advanced cell representation with both appearance and spatial context. On various benchmarks, our method achieves better performance than state-of-the-arts, especially on the classification task. We also create a new dataset for multi-class cell detection and classification in breast cancer and we make both our code and data publicly available.
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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.