Cell Detection
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Benchmarks
Most implemented
On Complex Valued Convolutional Neural Networks
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
Cell Detection with Star-convex Polygons
Papers
ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions
Background and Objective: Quantitative analysis of cell dynamics is central to modern biological research, providing critical insights into immune cell interactions, disease progression, and drug mechanisms. Automated ce…
Cell DetectionTwo-Stage Cross-Domain Cervical Abnormality Screening with Cytopathological Image Synthesis and Knowledge Distillation
Cross-domain diagnosis remains a major challenge in cervical cell pathology due to pronounced domain shifts across institutions and the subtle visual differences among disease stages, which jointly impair model generaliz…
Knowledge DistillationCell DetectionDualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection
Cell detection in histopathology images strongly depends on surrounding tissue context, where visually similar cells may belong to different classes under different microenvironments. Recent tissue-aware methods incorpor…
Cell DetectionA Multi-Stage Optimization Pipeline for Bethesda Cell Detection in Pap Smear Cytology
Computer vision techniques have advanced significantly in recent years, finding diverse and impactful applications within the medical field. In this paper, we introduce a new framework for the detection of Bethesda cells…
Cell DetectionOpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characterization from routine hematoxylin and eosin…
Cell DetectionNeedle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology
In computational cytology, detecting malignancy on whole-slide images is difficult because malignant cells are morphologically diverse yet vanishingly rare amid a vast background of normal cells. Accurate detection of th…
Multiple Instance LearningRepresentation LearningContrastive LearningCell Detection