Papers Cell Detection
“Cell Detection” 태그가 달린 논문 154편 · 필터 해제
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 DetectionEfficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage
Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuable tool to study the abundance and species richness of these insects, off…
Cell DetectionAn automatic counting algorithm for the quantification and uncertainty analysis of the number of microglial cells trainable in small and heterogeneous datasets
Counting immunopositive cells on biological tissues generally requires either manual annotation or (when available) automatic rough systems, for scanning signal surface and intensity in whole slide imaging. In this work,…
Cell DetectionCellMamba: Adaptive Mamba for Accurate and Efficient Cell Detection
Cell detection in pathological images presents unique challenges due to densely packed objects, subtle inter-class differences, and severe background clutter. In this paper, we propose CellMamba, a lightweight and accura…
Cell DetectionTransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals
Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals…
Multi-Object TrackingCell DetectionDiffusion-Based Synthetic Brightfield Microscopy Images for Enhanced Single Cell Detection
Accurate single cell detection in brightfield microscopy is crucial for biological research, yet data scarcity and annotation bottlenecks limit the progress of deep learning methods. We investigate the use of uncondition…
Synthetic Data GenerationObject DetectionCell DetectionGeneralizable Blood Cell Detection via Unified Dataset and Faster R-CNN
This paper presents a comprehensive methodology and comparative performance analysis for the automated classification and object detection of peripheral blood cells (PBCs) in microscopic images. Addressing the critical c…
Transfer LearningObject DetectionCell DetectionIn-Context Adaptation of VLMs for Few-Shot Cell Detection in Optical Microscopy
Foundation vision-language models (VLMs) excel on natural images, but their utility for biomedical microscopy remains underexplored. In this paper, we investigate how in-context learning enables state-of-the-art VLMs to …
Few-Shot Object DetectionCell DetectionComprehensive Benchmarking of YOLOv11 Architectures for Scalable and Granular Peripheral Blood Cell Detection
Manual peripheral blood smear (PBS) analysis is labor intensive and subjective. While deep learning offers a promising alternative, a systematic evaluation of state of the art models such as YOLOv11 for fine grained PBS …
Computational EfficiencyObject DetectionCell DetectionFrom Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction
Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. While artificial intelligence (AI) solutio…
Cell DetectionOCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. …
Cell DetectionIs Synthetic Image Augmentation Useful for Imbalanced Classification Problems? Case-Study on the MIDOG2025 Atypical Cell Detection Competition
The MIDOG 2025 challenge extends prior work on mitotic figure detection by introducing a new Track 2 on atypical mitosis classification. This task aims to distinguish normal from atypical mitotic figures in histopatholog…
Image AugmentationCell DetectionHigh-Precision Mixed Feature Fusion Network Using Hypergraph Computation for Cervical Abnormal Cell Detection
Automatic detection of abnormal cervical cells from Thinprep Cytologic Test (TCT) images is a critical component in the development of intelligent computer-aided diagnostic systems. However, existing algorithms typically…
Cell DetectionTowards Comprehensive Cellular Characterisation of H&E slides
Cell detection, segmentation and classification are essential for analyzing tumor microenvironments (TME) on hematoxylin and eosin (H&E) slides. Existing methods suffer from poor performance on understudied cell types (r…
Domain GeneralizationCell DetectionABCD: Automatic Blood Cell Detection via Attention-Guided Improved YOLOX
Detection of blood cells in microscopic images has become a major focus of medical image analysis, playing a crucial role in gaining valuable insights into a patient's health. Manual blood cell checks for disease detecti…
Cell Detection