Nuclear Segmentation
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Benchmarks
Cell17
Most implemented
Image-to-Image Translation with Conditional Adversarial Networks
Mask R-CNN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Towards Large-Scale Training of Pathology Foundation Models
Papers
VitaminP: 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 SegmentationBenchmarking Computational Pathology Foundation Models For Semantic Segmentation
In recent years, foundation models such as CLIP, DINO,and CONCH have demonstrated remarkable domain generalization and unsupervised feature extraction capabilities across diverse imaging tasks. However, systematic and in…
Semantic SegmentationDomain GeneralizationNuclear SegmentationA Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis
The development of clinical-grade artificial intelligence in pathology is limited by the scarcity of diverse, high-quality annotated datasets. Generative models offer a potential solution but suffer from semantic instabi…
Visual Question AnsweringSelf-Supervised LearningCross-Modal RetrievalNuclear SegmentationSynthetic-to-Real Transfer Learning for Chromatin-Sensitive PWS Microscopy
Chromatin sensitive partial wave spectroscopic (csPWS) microscopy enables label free detection of nanoscale chromatin packing alterations that occur before visible cellular transformation. However, manual nuclear segment…
Nuclear SegmentationTransfer LearningMorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification
Domain generalization in computational histopathology is hindered by heterogeneity in whole slide images (WSIs), caused by variations in tissue preparation, staining, and imaging conditions across institutions. Unlike ma…
Representation LearningDomain GeneralizationCancer ClassificationNuclear SegmentationUnpaired Image-to-Image Translation for Segmentation and Signal Unmixing
This work introduces Ui2i, a novel model for unpaired image-to-image translation, trained on content-wise unpaired datasets to enable style transfer across domains while preserving content. Building on CycleGAN, Ui2i inc…
Domain AdaptationImage-to-Image TranslationNuclear SegmentationStyle Transfer+1