Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges | Paper Citation Record

Observation e4e16326-8310-4a8c-ab62-93b18124c001 · outbound

This paper cites Synthetic data in biomedicine via generative artificial intelligence,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Synthetic data in biomedicine via generative artificial intelligence,

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This paper cites Shifting machine learning for healthcare from development to deployment and from models to data,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Shifting machine learning for healthcare from development to deployment and from models to data,

Reference 2

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Observation 4dc9da61-255b-4ecd-95dc-dc8d977a6a64 · outbound

This paper cites Diffusion models in medical imaging: A compre- hensive survey,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion models in medical imaging: A compre- hensive survey,

Reference 3

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Observation 800f0b2e-c84f-4a49-8436-21ab075691b4 · outbound

This paper cites Generative ai for computational pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generative ai for computational pathology,

Reference 4

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Observation b9a74027-c8b2-4987-ad56-df9a9019b693 · outbound

This paper cites Artificial intelligence for digital and computational pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Artificial intelligence for digital and computational pathology,

Reference 5

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Observation 2930bc67-f6f2-4413-9b3b-7fd6cd197c0e · outbound

This paper cites Diffusion models in vision: A survey,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion models in vision: A survey,

Reference 6

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Observation 7d7bb908-1683-4aee-b195-191aac9938ce · outbound

This paper cites Pix2path: Integrating spatial transcriptomics and digital pathology with deep learning to score pathological risk and link gene expression to disease mechanisms,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Pix2path: Integrating spatial transcriptomics and digital pathology with deep learning to score pathological risk and link gene expression to disease mechanisms,

Reference 7

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Observation 7b2fc428-b019-49e1-8135-7f83c6fa2580 · outbound

This paper cites Generative adversarial networks accurately recon- struct pan-cancer histology from pathologic, genomic, and radiographic latent features,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generative adversarial networks accurately recon- struct pan-cancer histology from pathologic, genomic, and radiographic latent features,

Reference 8

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Observation 57ef02e0-a81f-422a-a9ad-842d3e4ef5c0 · outbound

This paper cites Artifact detection and restoration in histology images with stain-style and structural preservation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Artifact detection and restoration in histology images with stain-style and structural preservation,

Reference 9

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Observation 58df4ec4-309c-48a0-87f1-eaf7e82662b0 · outbound

This paper cites ODA-GAN: Orthogonal decoupling alignment GAN assisted by weakly-supervised learning for virtual immunohistochem- istry staining,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges ODA-GAN: Orthogonal decoupling alignment GAN assisted by weakly-supervised learning for virtual immunohistochem- istry staining,

Reference 10

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Observation d56f6ebd-58de-433f-a4c5-3289373d4387 · outbound

This paper cites Cross-modal diffusion modelling for super-resolved spatial transcriptomics,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Cross-modal diffusion modelling for super-resolved spatial transcriptomics,

Reference 11

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Observation c7033411-a82c-4e31-bd11-9b84e546d665 · outbound

This paper cites GenST: A generative cross-modal model for predicting spatial transcriptomics from histology images,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges GenST: A generative cross-modal model for predicting spatial transcriptomics from histology images,

Reference 12

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Observation 47c7219e-07ef-4e7b-87a2-1dffa46467fb · outbound

This paper cites Integrating spatial and single-cell transcriptomics data using deep generative models with spatialscope,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Integrating spatial and single-cell transcriptomics data using deep generative models with spatialscope,

Reference 13

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Observation 30484139-9ad7-444f-a8ee-34b04fcec017 · outbound

This paper cites Self-supervised representation learning using visual field expansion on digital pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Self-supervised representation learning using visual field expansion on digital pathology,

Reference 14

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Observation d9afdcaa-6366-46f3-8c0c-51e59594b3d8 · outbound

This paper cites Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology,

Reference 15

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Observation 4942684b-d51a-46df-80d3-a04084c21df9 · outbound

This paper cites Artifact restoration in histology images with diffusion probabilistic models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Artifact restoration in histology images with diffusion probabilistic models,

Reference 16

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Observation f67508ab-4870-4880-a735-e91521c4806a · outbound

This paper cites StainDiff: Transfer stain styles of histology images with denoising diffusion probabilistic models and self-ensemble,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges StainDiff: Transfer stain styles of histology images with denoising diffusion probabilistic models and self-ensemble,

Reference 17

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Observation 050b2bde-c2af-4306-96c4-165f0864584d · outbound

This paper cites A multimodal generative ai copilot for human pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A multimodal generative ai copilot for human pathology,

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Observation f2b5cc5d-714e-4686-be43-1ea9dea8c536 · outbound

This paper cites SlideChat: A large vision-language assistant for whole-slide pathology image understanding,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges SlideChat: A large vision-language assistant for whole-slide pathology image understanding,

Reference 19

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Observation e31754aa-c87a-4140-89d6-d920cf22c13e · outbound

This paper cites Quilt-LLaV A: Visual instruction tuning by ex- tracting localized narratives from open-source histopathology videos,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Quilt-LLaV A: Visual instruction tuning by ex- tracting localized narratives from open-source histopathology videos,

Reference 20

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Observation 2b989b0a-b4a0-4b32-b39b-f633717b3ebd · outbound

This paper cites Diffusion models in low-level vision: A survey,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion models in low-level vision: A survey,

Reference 21

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Observation 7c7a1756-7222-42fa-b449-28d189e13ae1 · outbound

This paper cites Diffusion models in bioinformatics and computational biology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion models in bioinformatics and computational biology,

Reference 22

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Observation 500f715c-b55b-410b-8fc2-e9dd8bb2cb01 · outbound

This paper cites A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models

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Observation 66c26798-3105-410a-b871-e276e97704c7 · outbound

This paper cites Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Reference 24

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Observation 88345437-edd7-4e28-a1a3-c98e3806e882 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Learning transferable visual models from natural language supervision,

Reference 25

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Observation 75ff1f23-2876-41c8-a383-87dc5405fdae · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Scaling up visual and vision-language representation learning with noisy text supervision,

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Observation 603f0402-a567-4d24-8850-b484bf76b33c · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges DINOv2: Learning Robust Visual Features without Supervision

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Observation 72a850c5-fae7-4ab5-abf9-af5731156f31 · outbound

This paper cites An introduction to variational autoen- coders,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges An introduction to variational autoen- coders,

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Observation 2411c1dd-837c-4ec3-99e9-2783b55501f9 · outbound

This paper cites Robust multimodal survival prediction with conditional latent differentiation variational autoencoder,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Robust multimodal survival prediction with conditional latent differentiation variational autoencoder,

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Observation 810984c0-c1f5-407a-b452-ce3e3d07f828 · outbound

This paper cites Self-supervised representation distribution learning for reliable data augmentation in histopathology WSI classification,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Self-supervised representation distribution learning for reliable data augmentation in histopathology WSI classification,

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Observation 1e77a11f-6fca-41c1-a12f-2fd793dcc4e7 · outbound

This paper cites Generative adversarial nets,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generative adversarial nets,

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This paper cites Conditional Generative Adversarial Nets.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Conditional Generative Adversarial Nets

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Observation 6a1b7269-28db-4693-9e64-3a8a699336ce · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Image-to-image translation with conditional adversarial networks,

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Observation 34b684f3-44ff-4d3a-a6af-869c15be40cf · outbound

This paper cites Tackling stain variability using CycleGAN-based stain augmentation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Tackling stain variability using CycleGAN-based stain augmentation,

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Observation 700b60ce-85e2-4e93-89a9-27f11f2781c7 · outbound

This paper cites Stain normalization of histopathology images using generative adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Stain normalization of histopathology images using generative adversarial networks,

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Observation f2a89467-9a0e-4669-8ddd-6d7f7ce57711 · outbound

This paper cites Generative adversarial networks for stain normalisation in histopathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generative adversarial networks for stain normalisation in histopathology,

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Observation 763b5679-8757-4c37-b23c-f0fc76f8ff4c · outbound

This paper cites Virtual multi-staining in a single-section view for renal pathology using generative adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Virtual multi-staining in a single-section view for renal pathology using generative adversarial networks,

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Observation 7758f9ee-a3c5-4f8d-aa2d-4ffbf6fe4521 · outbound

This paper cites Unpaired stain transfer using pathology-consistent constrained generative adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Unpaired stain transfer using pathology-consistent constrained generative adversarial networks,

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Observation c24c5f93-4d95-4394-93f0-6afe78975e9e · outbound

This paper cites A federated learning system for histopathology image analysis with an orchestral stain-normalization GAN,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A federated learning system for histopathology image analysis with an orchestral stain-normalization GAN,

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This paper cites A style-based generator architecture for generative adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A style-based generator architecture for generative adversarial networks,

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Observation f9bd68cd-cc83-4d4b-bab3-ff9c5ae61c11 · outbound

This paper cites Characterizing the features of mitotic figures using a conditional diffusion probabilistic model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Characterizing the features of mitotic figures using a conditional diffusion probabilistic model,

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Observation 69b206a7-e9e2-4709-a216-d03e98595697 · outbound

This paper cites ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis,

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This paper cites Co-synthesis of histopathology nuclei image-label pairs using a context-conditioned joint diffusion model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Co-synthesis of histopathology nuclei image-label pairs using a context-conditioned joint diffusion model,

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This paper cites Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks,

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This paper cites Denoising diffusion probabilistic models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Denoising diffusion probabilistic models,

Reference 45

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This paper cites Denoising diffusion implicit models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Denoising diffusion implicit models,

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This paper cites Score-based generative modeling through stochastic differential equations,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Score-based generative modeling through stochastic differential equations,

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This paper cites Diffusion-based generation of histopathological whole slide images at a gigapixel scale,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion-based generation of histopathological whole slide images at a gigapixel scale,

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This paper cites Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets,

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This paper cites Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment

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This paper cites Histology image artifact restoration with lightweight transformer based diffusion model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Histology image artifact restoration with lightweight transformer based diffusion model,

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This paper cites Disc: Latent diffusion models with self-distillation from separated conditions for prostate cancer grading,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Disc: Latent diffusion models with self-distillation from separated conditions for prostate cancer grading,

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This paper cites NASDM: Nuclei-aware semantic histopathology image generation using diffusion models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges NASDM: Nuclei-aware semantic histopathology image generation using diffusion models,

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This paper cites Generation of synthetic whole-slide image tiles of tumours from rna-sequencing data via cascaded diffusion models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generation of synthetic whole-slide image tiles of tumours from rna-sequencing data via cascaded diffusion models,

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Observation 9f18db25-0bd6-4054-acbb-11eb8ec8f134 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Classifier-Free Diffusion Guidance

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This paper cites High-resolution image synthesis with latent diffu- sion models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges High-resolution image synthesis with latent diffu- sion models,

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This paper cites Adding conditional control to text-to-image diffusion models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Adding conditional control to text-to-image diffusion models,

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This paper cites Counterfactual diffusion models for mechanistic explainability of artificial intelligence models in pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Counterfactual diffusion models for mechanistic explainability of artificial intelligence models in pathology,

Reference 58

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This paper cites An Introduction to Vision-Language Modeling.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges An Introduction to Vision-Language Modeling

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This paper cites Learning visual grounding from generative vision and language model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Learning visual grounding from generative vision and language model,

Reference 60

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This paper cites Cplip: Zero-shot learning for histopathology with comprehensive vision-language alignment,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Cplip: Zero-shot learning for histopathology with comprehensive vision-language alignment,

Reference 61

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Pathalign: A vision-language model for whole slide images in histopathology,

Reference 62

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Visual instruction tuning,

Reference 63

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This paper cites Vision-language transformer for interpretable pathol- ogy visual question answering,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Vision-language transformer for interpretable pathol- ogy visual question answering,

Reference 64

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Observation d1937b21-2c34-4eae-a281-58df4b986430 · outbound

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Towards generalist biomedical ai,

Reference 65

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges LLaV A-med: Training a large language-and-vision assis- tant for biomedicine in one day,

Reference 66

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This paper cites Generating dermatopathology reports from gigapixel whole slide images with HistoGPT,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generating dermatopathology reports from gigapixel whole slide images with HistoGPT,

Reference 67

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Observation 5f8c82a9-8435-439c-9542-1d20e880c703 · outbound

This paper cites Pathologyvlm: a large vision-language model for pathology image understanding,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Pathologyvlm: a large vision-language model for pathology image understanding,

Reference 68

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Topofm: Topology-guided pathology foundation model for high-resolution pathology image synthesis with cellular-level control,

Reference 69

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Observation 55f219df-1482-498b-a4e0-97636b5e0309 · outbound

This paper cites A multimodal knowledge-enhanced whole-slide pathology foundation model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A multimodal knowledge-enhanced whole-slide pathology foundation model,

Reference 70

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Observation 64137bf3-2829-4f4d-acab-f0599033a5e5 · outbound

This paper cites Pathoduet: Foundation models for pathological slide analysis of h&e and ihc stains,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Pathoduet: Foundation models for pathological slide analysis of h&e and ihc stains,

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Observation 78230f1b-cdf9-4615-891f-9b08d44fa896 · outbound

This paper cites Star-rl: Spatial-temporal hierarchical reinforcement learning for interpretable pathology image super-resolution,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Star-rl: Spatial-temporal hierarchical reinforcement learning for interpretable pathology image super-resolution,

Reference 72

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This paper cites Evidence-based diagnostic reasoning with multi-agent copilot for human pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Evidence-based diagnostic reasoning with multi-agent copilot for human pathology,

Reference 73

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology

Reference 74

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Selective synthetic augmentation with histogan for im- proved histopathology image classification,

Reference 75

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Unified framework for histopathology image augmenta- tion and classification via generative models,

Reference 76

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This paper cites Mitigating bias in prostate cancer diagnosis using synthetic data for improved ai driven gleason grading,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Mitigating bias in prostate cancer diagnosis using synthetic data for improved ai driven gleason grading,

Reference 77

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Insmix: Towards realistic generative data augmentation for nuclei instance segmentation,

Reference 78

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Vit-dae: Transformer-driven diffusion autoencoder for histopathology image analysis,

Reference 79

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion-based data augmentation for nuclei image segmentation,

Reference 80

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Usegmix: Unsupervised segment mix for efficient data augmentation in pathology images,

Reference 81

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Observation 76864504-de8b-4906-b8d8-8945e5bccb92 · outbound

This paper cites Learned representation-guided diffusion models for large-image generation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Learned representation-guided diffusion models for large-image generation,

Reference 82

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This paper cites Prototype-guided diffusion for digital pathology: Achieving foundation model performance with minimal clinical data,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Prototype-guided diffusion for digital pathology: Achieving foundation model performance with minimal clinical data,

Reference 83

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This paper cites Generating progressive images from pathological tran- sitions via diffusion model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generating progressive images from pathological tran- sitions via diffusion model,

Reference 84

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Optimising diffusion models for histopathology image synthesis,

Reference 85

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This paper cites Pdseg: Patch-wise distillation and controllable image generation for weakly-supervised histopathology tissue segmentation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Pdseg: Patch-wise distillation and controllable image generation for weakly-supervised histopathology tissue segmentation,

Reference 86

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This paper cites Enhancing gland segmentation in colon histology images using an instance-aware diffusion model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Enhancing gland segmentation in colon histology images using an instance-aware diffusion model,

Reference 87

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Observation 94647f8d-614a-4386-bc54-0e2b2563d736 · outbound

This paper cites Diffusion models for out-of-distribution detection in digital pathology,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Diffusion models for out-of-distribution detection in digital pathology,

Reference 88

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges PathologyGAN: Learning deep representations of cancer tissue

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This paper cites Multi-scale self-attention generative adversarial net- work for pathology image restoration,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Multi-scale self-attention generative adversarial net- work for pathology image restoration,

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A morphology focused diffusion probabilistic model for synthesis of histopathology images,

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Observation 5ac61b96-e2b8-4575-8242-f9b9588a9be4 · outbound

This paper cites Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Deep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model,

Reference 92

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Observation 63ee4b71-57a2-4cf8-83a1-c3c09f63618d · outbound

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generative models improve fairness of medical classifiers under distribution shifts,

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This paper cites Generating and evaluating synthetic data in digital pathology through diffusion models,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Generating and evaluating synthetic data in digital pathology through diffusion models,

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This paper cites A multi-attribute controllable generative model for histopathology image synthesis,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges A multi-attribute controllable generative model for histopathology image synthesis,

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Sharp-gan: Sharpness loss regularized gan for histopathology image synthesis,

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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Realistic data enrichment for robust image segmentation in histopathology,

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This paper cites Style-extracting diffusion models for semi-supervised histopathology segmentation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Style-extracting diffusion models for semi-supervised histopathology segmentation,

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This paper cites Synclay: Interactive synthesis of histology images from bespoke cellular layouts,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Synclay: Interactive synthesis of histology images from bespoke cellular layouts,

Reference 99

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This paper cites Hadiff: hierarchy aggregated diffusion model for pathology image segmentation,.

Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges Hadiff: hierarchy aggregated diffusion model for pathology image segmentation,

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