CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

📢 November 2025 — CORE Released as Open-Source The first public release of CORE, a unified coarse-to-fine multi-stain image registration engine, is now available. This release includes prompt-guided mask generation, accelerated features based coarse alignment, nuclei-level refinement, and real-time deformation visualization.

📝 November 2025 — Updated Preprint Available on arXiv. The team has released an updated version of the CORE preprint, expanding on the architecture, benchmarks, and qualitative results. Check out the newest version here: arXiv:2403.05780.

🎥 New TIAViz Integration Demo - Added a full registration workflow demo showing real-time deformation fields and alignment quality inside TIAViz, enabling seamless analysis for whole-slide images.

🧪 Sample Notebooks Added - End-to-end Jupyter notebooks for coarse and fine alignment have been added, making it easier for users to experiment with CORE immediately.

Introduction

CORE is a fast and accurate coarse-to-fine image registration engine designed for aligning multi-stain whole-slide images. It combines prompt-based tissue masking, rapid coarse alignment, and nuclei-level fine registration to deliver precise cell-level correspondence across stains. With real-time deformation visualization and easy integration, CORE enables reliable multi-stain analysis for digital pathology workflows.

Features

Prompt-based Tissue Mask Extraction.
Fast coarse level multi-stain image registration.
Fine-grained Nuclei-level precise alignment on re-stained sections and tissue alignment on consecutive sections.
Real time deformation estimation and Registration visualisation.

CORE VISUALIZATION

Installation

Clone the repo.
change directory to project directory
Create conda enivornment for installing the required dependencies using the following command
```
conda env create -f environment.yml
conda activate core
```

Set API Keys as Environment Variables

For our prompt-based tissue mask generation. You must set the VisionAgent API key as environment variables. Each operating system offers different ways to do this. Here is the code for setting the variables:

export VISION_AGENT_API_KEY="your-api-key"

For UNet based tissue mask extraction we have made the weights publicly available on hugging face. CORE

Configuration

Edit config.py to set your file paths and resolution parameters:

# Update these paths to match your dataSOURCE_WSI_PATH="/path/to/your/source_wsi.tiff"TARGET_WSI_PATH="/path/to/your/target_wsi.tiff"

Usage

Example of both coarse and fine registration have been placed under the notebooks folder.

How to Cite

@misc{nasir2025corecelllevelcoarsetofine,
title={CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment}, author={Esha Sadia Nasir and Behnaz Elhaminia and Mark Eastwood and Catherine King and Owen Cain and Lorraine Harper and Paul Moss and Dimitrios Chanouzas and David Snead and Nasir Rajpoot and Adam Shephard and Shan E Ahmed Raza},
year={2025},
eprint={2511.03826},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2511.03826}, }

CORE Registration DEMO

demo.mov

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
accelerated_features		accelerated_features
core		core
docs		docs
notebooks		notebooks
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
ablation_study.py		ablation_study.py
apply_deformation_wsi.py		apply_deformation_wsi.py
batch_run.py		batch_run.py
environment.yml		environment.yml
example.py		example.py
requirements.txt		requirements.txt
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
accelerated_features		accelerated_features
core		core
docs		docs
notebooks		notebooks
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
ablation_study.py		ablation_study.py
apply_deformation_wsi.py		apply_deformation_wsi.py
batch_run.py		batch_run.py
environment.yml		environment.yml
example.py		example.py
requirements.txt		requirements.txt
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
accelerated_features		accelerated_features
core		core
docs		docs
notebooks		notebooks
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
ablation_study.py		ablation_study.py
apply_deformation_wsi.py		apply_deformation_wsi.py
batch_run.py		batch_run.py
environment.yml		environment.yml
example.py		example.py
requirements.txt		requirements.txt
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
accelerated_features		accelerated_features
core		core
docs		docs
notebooks		notebooks
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
ablation_study.py		ablation_study.py
apply_deformation_wsi.py		apply_deformation_wsi.py
batch_run.py		batch_run.py
environment.yml		environment.yml
example.py		example.py
requirements.txt		requirements.txt
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
accelerated_features		accelerated_features
core		core
docs		docs
notebooks		notebooks
tests		tests
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
ablation_study.py		ablation_study.py
apply_deformation_wsi.py		apply_deformation_wsi.py
batch_run.py		batch_run.py
environment.yml		environment.yml
example.py		example.py
requirements.txt		requirements.txt
setup.py		setup.py

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CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

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Introduction

Features

CORE VISUALIZATION

Installation

Set API Keys as Environment Variables

Configuration

Usage

How to Cite

CORE Registration DEMO

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CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

Introduction

Features

CORE VISUALIZATION

Installation

Set API Keys as Environment Variables

Configuration

Usage

How to Cite

CORE Registration DEMO

About

Resources

Contributing

Stars

Watchers

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Releases

Packages

Contributors

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CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

News

Introduction

Features

CORE VISUALIZATION

Installation

Set API Keys as Environment Variables

Configuration

Usage

How to Cite

CORE Registration DEMO

About

Resources

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

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Latest commit