Papers Cell Segmentation
“Cell Segmentation” 태그가 달린 논문 210편 · 필터 해제
Compositional Reward Models for Conditional Medical Image Generation
Acquiring high quality annotated medical image data is critical for training deep learning models; however, annotation is expensive, time consuming, and requires domain expertise. Conditional diffusion models, such as Co…
Skin Lesion ClassificationMedical Image GenerationReinforcement LearningCell SegmentationQCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation
Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structures that produce weak boundaries and mixed visual evidence in overlap regions. Existing methods address this thr…
Instance SegmentationCell SegmentationMorphology-Aware Implicit Super-Resolution Network for Pathological Images
Accurate diagnosis in Digital Pathology (DP) relies on high-resolution whole-slide images, yet clinical deployment is often limited by hardware costs. Super-Resolution (SR) offers a promising alternative by computational…
Cell SegmentationSAFViT: Spatial Attention Fusion Gating for Vision Transformer-Based Nucleus Segmentation and Classification
Accurate cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment planning. Encoder-decoder architectures that fuse multi-scale feature…
Cell SegmentationMalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of expert microscopists remains the primary bottleneck to timely and accurat…
Image ClassificationCell SegmentationRethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation
Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remarkable universal feature representations, unlocking their full potent…
Cell Segmentation$μ$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have been successfully applied to biomedical imaging tasks ranging from org…
Cell SegmentationDomain AdaptationSegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extraction, and interpretable clinical reporting. We present SegTME-UNI2, a unif…
Semantic SegmentationCell SegmentationDART: A design-aware microfluidic chip paradigm for real-time live-cell image analysis
High-throughput microfluidic live-cell imaging generates rich single-cell data. Yet semi-automated procedures for locating regions of interest (RoIs), each containing one cell population, and removing surrounding microfl…
Cell SegmentationImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations
Accurate cell segmentation in pathology images typically requires dense pixel-wise annotations, which are costly and time-consuming to obtain. This challenge is especially important for emerging biological imaging modali…
Cell SegmentationVesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images
Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment-then-fix paradigm is fundamentally limited regarding its suitabilit…
Cell SegmentationVitaminP: cross-modal learning enables whole-cell segmentation from routine histology
Accurate whole-cell and nuclear segmentation is essential for precision pathology and spatial omics, yet routine hematoxylin and eosin (H&E) staining provides limited cytoplasmic contrast, restricting analyses to nuclei.…
Nuclear SegmentationCell SegmentationSelf-supervised Pretraining of Cell Segmentation Models
Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each cell. However, progress is constrained by the scarcity of high-quali…
Instance SegmentationCell SegmentationIdentity-Aware U-Net: Fine-grained Cell Segmentation via Identity-Aware Representation Learning
Precise segmentation of objects with highly similar shapes remains a challenging problem in dense prediction, especially in scenarios with ambiguous boundaries, overlapping instances, and weak inter-instance visual diffe…
Representation LearningObject SegmentationCell SegmentationMetric LearningUCell: rethinking generalizability and scaling of bio-medical vision models
The modern deep learning field is a scale-centric one. Larger models have been shown to consistently perform better than smaller models of similar architecture. In many sub-domains of biomedical research, however, the mo…
Cell SegmentationCytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report
Computational pathology has made significant progress in recent years, fueling advances in both fundamental disease understanding and clinically ready tools. This evolution is driven by the availability of large amounts …
Cell SegmentationRevisiting foundation models for cell instance segmentation
Cell segmentation is a fundamental task in microscopy image analysis. Several foundation models for cell segmentation have been introduced, virtually all of them are extensions of Segment Anything Model (SAM), improving …
Instance SegmentationCell SegmentationPersonalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation
Accurate cell segmentation is critical for biological and medical imaging studies. Although recent deep learning models have advanced this task, most methods are limited to generic cell segmentation, lacking the ability …
Contrastive LearningCell SegmentationTowards Instance Segmentation with Polygon Detection Transformers
One of the bottlenecks for instance segmentation today lies in the conflicting requirements of high-resolution inputs and lightweight, real-time inference. To address this bottleneck, we present a Polygon Detection Trans…
Instance SegmentationCell SegmentationConformal Prediction Sets for Instance Segmentation
Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is c…
Instance SegmentationCell Segmentation