Added model checkpoints and updated README

LICENSE

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+MIT License
+
+Copyright (c) 2024 Fabian Reith
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-license: mit
----
+# APEDIA Model Weights
+
+## Overview
+This repository hosts the model weights for [APEDIA](https://github.com/Kainmueller-Lab/APEDIA) (Angiosarcoma PD-L1 Expression DIAgnostics with Deep Learning), a deep learning tool designed to assist pathologists in assessing PD-L1 expression in angiosarcoma through the calculation of the Tumor Proportion Score (TPS) from whole-slide images (WSIs).
+
+APEDIA aims to enhance decision-making quality in the pathology of angiosarcoma by automating the detection of tumor areas and the classification of cell types based on PD-L1 expression.
+
+## Model Checkpoints
+This repository contains two critical checkpoints for the APEDIA pipeline:
+
+- `tp_pred_model_checkpoint.pth`: This model checkpoint is responsible for detecting tumor areas in PD-L1 stained Whole-Slide Image (WSI) patches.
+
+- `seg_model_checkpoint.pth`: This checkpoint powers the detection and classification of cell types within tumor patches, distinguishing between background, non-tumor cells, PD-L1 positive cells, and PD-L1 negative cells.
+
+## Usage
+The model weights will be automatically downloaded by the APEDIA library when executing predictions to calculate the Tumor Proportion Score (TPS). Ensure that you have the latest version of APEDIA installed to use these models effectively.
+
+For detailed instructions on integrating these model weights with APEDIA and running the prediction pipeline, please refer to the [APEDIA repository](https://github.com/Kainmueller-Lab/APEDIA).
+
+## License
+The model weights are available under the MIT License. For more details, see the [LICENSE](LICENSE) file in this repository.
+

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