Papers Nuclear Segmentation
“Nuclear Segmentation” 태그가 달린 논문 38편 · 필터 해제
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+1APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification
Nuclear instance segmentation and classification provide critical quantitative foundations for digital pathology diagnosis. With the advent of the foundational Segment Anything Model (SAM), the accuracy and efficiency of…
ClassificationInstance SegmentationNuclear SegmentationSemantic SegmentationFrGNet: A fourier-guided weakly-supervised framework for nuclear instance segmentation
Nuclear instance segmentation has played a critical role in pathology image analysis. The main challenges arise from the difficulty in accurately segmenting instances and the high cost of precise mask-level annotations f…
Instance SegmentationNuclear SegmentationSemantic SegmentationAre nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment.…
AllCancer ClassificationData AugmentationNuclear SegmentationHoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images
In computational digital pathology, accurate nuclear segmentation of Hematoxylin and Eosin (H&E) stained whole slide images (WSIs) is a critical step for many analyses and tissue characterizations. One popular deep learn…
GPUKnowledge DistillationNuclear SegmentationSegmentation+1Towards Large-Scale Training of Pathology Foundation Models
Driven by the recent advances in deep learning methods and, in particular, by the development of modern self-supervised learning algorithms, increased interest and efforts have been devoted to build foundation models (FM…
Nuclear SegmentationSelf-Supervised Learningwhole slide imagesCoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wid…
Nuclear SegmentationSegmentationSurvival Analysiswhole slide imagesNuclear Segmentation and Classification: On Color & Compression Generalization
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution …
ClassificationNuclear SegmentationStyle TransferSynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histo…
Image GenerationNuclear SegmentationCellular Segmentation and Composition in Routine Histology Images using Deep Learning
Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred t…
ManagementNuclear SegmentationPrognosisSegmentationA Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity and wide variations. This work proposes a…
ClassificationNuclear SegmentationSegmentationSeparable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…
ClassificationExplainable ModelsNuclear SegmentationSegmentationA Standardized Pipeline for Colon Nuclei Identification and Counting Challenge
Nuclear segmentation and classification is an essential step for computational pathology. TIA lab from Warwick University organized a nuclear segmentation and classification challenge (CoNIC) for H&E stained histopatholo…
ClassificationData AugmentationNuclear SegmentationSegmentationSimultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational patholog…
Instance SegmentationNuclear SegmentationSegmentationSemantic SegmentationNuclei panoptic segmentation and composition regression with multi-task deep neural networks
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…
DecoderExplainable ModelsMulti-Task LearningNuclear Segmentation+3