VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
PanNuke Dataset Extension, Insights and Baselines,
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SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.
Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
CellDETR is a detection-guided framework extending Deformable DETR for cell representation learning from WSIs, with contrastive pretraining and cross-dataset transfer shown on PanNuke and Xenium data.
SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
PathAR factorizes structure and appearance tokens via Dual-VQ and IAR transformer for modality-conditioned pathology image synthesis with improved structural consistency.
Biological spatial priors based on MSI histology, when injected into TransMIL with foundation model features, improve cross-site generalization for MSI prediction from H&E WSIs, with peripheral distance encoding achieving high internal AUC and perfect external specificity.
CellPrior-Net integrates hematoxylin channel prior into a lightweight CNN for nuclei detection and classification in H&E WSIs, claiming comparable accuracy to SOTA with significantly reduced inference time across 10.4M nuclei from diverse datasets.
OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.
citing papers explorer
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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SAM 3: Segment Anything with Concepts
SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.
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Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.
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Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
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CellDETR: A Detection-Guided Framework for Scalable Cell Representation Learning from Histopathology Images
CellDETR is a detection-guided framework extending Deformable DETR for cell representation learning from WSIs, with contrastive pretraining and cross-dataset transfer shown on PanNuke and Xenium data.
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SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
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PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images
PathAR factorizes structure and appearance tokens via Dual-VQ and IAR transformer for modality-conditioned pathology image synthesis with improved structural consistency.
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Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancer
Biological spatial priors based on MSI histology, when injected into TransMIL with foundation model features, improve cross-site generalization for MSI prediction from H&E WSIs, with peripheral distance encoding achieving high internal AUC and perfect external specificity.
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CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images
CellPrior-Net integrates hematoxylin channel prior into a lightweight CNN for nuclei detection and classification in H&E WSIs, claiming comparable accuracy to SOTA with significantly reduced inference time across 10.4M nuclei from diverse datasets.
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OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.