initial commit for CARTA

Co-authored-by: LzucaXD <LzucaXD@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- tissue-microarray
+- multiplex-immunofluorescence
+- computer-vision
+- pytorch
+library_name: carta
+pipeline_tag: image-segmentation
+---
+# CARTA: Core and Tissue Detection weights
+
+CARTA’s tissue model is a DeepLabV3-ResNet50 segmenter for tissue vs background in multiplex immunofluorescence nuclear images. It was trained on annotated data primarily CODEX and is intended for fluorescence multiplex IF tissue segmentation
+It has been tested on: CyCIF, CODEX, Keyence, Orion, Vectra, PhenoCycler.
+
+CARTA can be imported from **[CORAL](https://github.com/mahmoodlab/CORAL)**.
+
+Pretrained weights for **[CARTA](https://github.com/mahmoodlab/CARTA)** (TMA core detection + tissue segmentation for multiplex immunofluorescence).
+
+| File | Task | Architecture | Size |
+|------|------|--------------|------|
+| `carta_core.pt` | TMA core bounding boxes | YOLOv8n | ~6 MB |
+| `carta_tissue.pt` | Tissue vs background | DeepLabV3-ResNet50 | ~161 MB |
+
+## Usage
+
+Weights download automatically when you install CARTA and run the pipeline:
+
+```python
+from segmenter.weights import resolve_weights
+detector = resolve_weights("detector")
+segmenter = resolve_weights("segmenter")
+```
+
+Or explicitly:
+
+```python
+from huggingface_hub import hf_hub_download
+detector = hf_hub_download("MahmoodLab/CARTA", "carta_core.pt")
+segmenter = hf_hub_download("MahmoodLab/CARTA", "carta_tissue.pt")
+```
+
+## Detector (YOLO)
+
+- Input: 1280×1280 letterboxed nuclear RGB canvas
+- Default inference: `conf=0.25`, `iou=0.7`, `agnostic_nms=True`
+
+## Segmenter (DeepLab)
+
+- Internal compute: **1 µm/px** (native crops resampled, masks upsampled to full resolution)
+- Tiled inference: 512×512, per-tile percentile normalization
+- Default export: DAPI + binary mask + GeoJSON per core
+
+## Intended use
+
+Tested on: CyCIF, CODEX, Keyence, Orion, Vectra, PhenoCycler.
+
+## License
+
+The CARTA weights are released under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/) license. They are provided for non-commercial academic research only; commercial use, or the distribution of derivative models, requires prior approval.
+
+## Citation
+
+Please cite the KRONOS paper:
+
+```bibtex
+@article{shaban2024foundation,
+  title        = {A Foundation Model for Spatial Proteomics},
+  author       = {Muhammad Shaban and Yuzhou Chang and Huaying Qiu and Yao Yu Yeo and Andrew H. Song and Guillaume Jaume and Yuchen Wang and Luca L. Weishaupt and Tong Ding and Anurag Vaidya and Abdallah Lamane and Daniel Shao and Mohammed Zidane and Yunhao Bai and Paige McCallum and Shuli Luo and Wenrui Wu and Yang Wang and Precious Cramer and Chi Ngai Chan and Pierre Stephan and Johanna Schaffenrath and Jia Le Lee and Hendrik A Michel and Caiwei Tian and Cristina Almagro-Perez and Sophia J. Wagner and Sharifa Sahai and Ming Y. Lu and Richard J. Chen and Andrew Zhang and Mark Edward M Gonzales and Ahmad Makky and Joey Lee and Hao Cheng and Maximilian Haist and Darci Phillips and Yuqi Tan and Garry P Nolan and W. Richard Burack and Jacob D Estes and Jonathan T.C. Liu and Toni K Choueiri and Neeraj Agarwal and Marc Barry and Scott J Rodig and Long Phi Le and Georg Gerber and Christian M. Schürch and Fabian J. Theis and Youn H Kim and Joe Yeong and Sabina Signoretti and Brooke Howitt and Lit-Hsin Loo and Qin Ma and Sizun Jiang and Faisal Mahmood},
+  year         = {2025},
+  note         = {Preprint},
+  howpublished = {\url{https://arxiv.org/abs/2506.03373}},
+}
+```

carta_core.pt

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carta_tissue.pt

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