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-# virtual_stain_flow
-For developing virtual staining models
+# `virtual_stain_flow` - For developing virtual staining models
+
+## Overview
+`virtual_stain_flow` is a framework for the reproducible development and training of image-to-image translation models that enable virtual staining (the prediction of "virtual" stains) from label-free microscopy images.  
+The package provides comprehensive experiment tracking that spans the entire model development workflow, from dataset construction, augmentation, model customization to training.
+
+---
+
+## Supported Model Architectures
+
+- **U-Net** – A classical encoder–decoder architecture with skip connections.  
+  This lightweight design has been widely used for virtual staining tasks, as demonstrated by [Ounkomol et al., 2018](https://doi.org/10.1038/s41592-018-0111-2).
+
+- **wGAN-GP** – A Wasserstein GAN with Gradient Penalty.  
+  This generative adversarial setup combines a U-Net generator with a convolutional discriminator regularized via gradient penalty for stable training.  
+  As shown by [Cross-Zamirski et al., 2022](https://doi.org/10.1038/s41598-022-12914-x), adversarial training enhances the realism of synthetic stains.
+
+- **ConvNeXt-UNet** – A fully convolutional architecture inspired by recent computer vision advances.  
+  Drawing from [Liu et al., 2022](https://doi.org/10.48550/arXiv.2201.03545) and [Liu et al., 2025](https://doi.org/10.1038/s42256-025-01046-2), this variant incorporates transformer-like architectural refinements to improve the fidelity of virtual staining details, at the cost of higher computational demand.
+
+### Showcasing of model prediction
+
+![Input/Target/Prediction](./assets/epoch_299.png)
+**Prediction (DNA, Hoechst 33342)** generated by `virtual_stain_flow` using the ConvNeXt-UNet model, from brightfield microscopy images of the U2-OS cell line.
+
+## Core Components
+
+- **[datasets/](./src/virtual_stain_flow/datasets/)** - Data loading and preprocessing pipelines
+- **[models/](./src/virtual_stain_flow/models/)** - Virtual staining models and building blocks
+- **[trainers/](./src/virtual_stain_flow/trainers/)** - Training loops
+- **[transforms/](./src/virtual_stain_flow/transforms/)** - Image normalization and augmnentation
+- **[vsf_logging/](./src/virtual_stain_flow/vsf_logging/)** - Experiment tracking and logging
+
+---
+
+## Quick Start
+
+Check out the **[examples/](./examples/)** directory for complete training scripts and tutorials demonstrating various use cases and configurations.
+
+Defining a UNet model generating staining of 3 target channels from 1 input (phase here).
+- Here the `compute_block` is set as the coventional Conv2D > Normalize > ReLU. 
+Alternative compute blocks are avaiable in this package, including the `Conv2DConvNeXtBlock`. 
+```python
+from virtual_stain_flow.models import UNet, Conv2DNormActBlock
+
+model = UNet(
+    input_channels=1,
+    output_channels=3,
+    comp_block=Conv2DNormActBlock,
+    _num_units=2,
+    depth=4
+)
+```
+
+
+Building a dataset tailored to the UNet model specification. 
+- The input channel is configured as `['phase']` which matches the `input_channels=1` of the model specification.
+- Likewise, the target channel is configured as `["dapi", "tubulin", "actin"]` to match the `output_channels=3` model setting.
+- Note these channel keys must exist in the supplied `file_index_df` as a column of image filepaths.
+- Specify the appropriate post-processing to match image bit depth and/or model output activation. 
+By default all models activate output with sigmoid and a maxscale normalization normalizing by max pixel value is used.  
+```python
+from virtual_stain_flow.datasets import BaseImageDataset
+from virtual_stain_flow.transforms import MaxScaleNormalize
+
+dataset = BaseImageDataset(
+    file_index=file_index_df,
+    input_channel_keys="phase",
+    target_channel_keys=["dapi", "tubulin", "actin"],
+    transform=MaxScaleNormalize(normalization_factor='16bit')
+)
+
+from virtual_stain_flow.trainers import Trainer
+from virtual_stain_flow.vsf_logging import MlflowLogger
+
+logger = MlflowLogger(experiment_name="virtual_staining")
+trainer = Trainer(model, dataset)
+trainer.train(logger=logger)
+```
+
+## Installation
+
+```bash
+pip install virtual-stain-flow
+```
+
+---
+
+## License
+
+See the [LICENSE](LICENSE) file for full details.
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