Update README.md

README.md

@@ -18,35 +18,35 @@ tags:
 
 ![OpenMind](https://huggingface.co/datasets/AnonRes/OpenMind/resolve/main/assets/OpenMindDataset.png)
 
-## 🔍 Overview
+## Overview
 
 This repository hosts pre-trained checkpoints from the **OpenMind** benchmark:  
-📄 **"An OpenMind for 3D medical vision self-supervised learning"**  
+📄 **An OpenMind for 3D medical vision self-supervised learning** (Wald, T., Ulrich, C., Suprijadi, J., Ziegler, S., Nohel, M., Peretzke, R., ... & Maier-Hein, K. H. (2024).)  
 ([arXiv:2412.17041](https://arxiv.org/abs/2412.17041)) — the first extensive benchmark study for **self-supervised learning (SSL)** on **3D medical imaging** data.
 
-The models were pre-trained using various SSL methods on the [OpenMind Dataset](https://huggingface.co/datasets/AnonRes/OpenMind), a large-scale, standardized collection of public brain MRI datasets.
+Each model was pre-trained using a particular SSL method on the [OpenMind Dataset](https://huggingface.co/datasets/AnonRes/OpenMind), a large-scale, standardized collection of public brain MRI datasets.
 
-**These models are not recommended to be used as-is.** Instead we recommend using the downstream fine-tuning pipelines for **segmentation** and **classification**, available in the [adaptation repository](https://github.com/TaWald/nnUNet).
-*While direct download is possible, we recommend using the auto-download of the respective fine-tuning repositories.*
+**These models are not recommended to be used as-is for feature extraction.** Instead we recommend using the downstream fine-tuning frameworks for **segmentation** and **classification** adaptation, available in the [adaptation repository](https://github.com/TaWald/nnUNet).
+*While manual download is possible, we recommend using the auto-download feature of the fine-tuning repository by providing the repository URL on Hugging Face instead of a local checkpoint path.*
 
 ---
 
-## 🧠 Model Variants
+## Model Variants
 
 We release SSL checkpoints for two backbone architectures:
 
-- **ResEnc-L**: A CNN-based encoder [[link1](https://arxiv.org/abs/2410.23132), [link2](https://arxiv.org/abs/2404.09556)]  
+- **ResEnc-L**: A CNN-based encoder [[a](https://arxiv.org/abs/2410.23132), [b](https://arxiv.org/abs/2404.09556)]  
 - **Primus-M**: A transformer-based encoder [[Primus paper](https://arxiv.org/abs/2503.01835)]
 
-Each encoder has been pre-trained using the following SSL techniques:
+Each encoder has been pre-trained using one of the following SSL techniques:
 
 | Method        | Description |
 |---------------|-------------|
-| [Volume Contrastive (VoCo)](https://arxiv.org/abs/2402.17300) | Global contrastive learning in 3D volumes |
-| [VolumeFusion (VF)](https://arxiv.org/abs/2306.16925)         | Spatial fusion-based SSL |
-| [Models Genesis (MG)](https://www.sciencedirect.com/science/article/pii/S1361841520302048) | Classic 3D self-reconstruction |
-| [Masked Autoencoders (MAE)](https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper) | Patch masking and reconstruction |
-| [Spark 3D (S3D)](https://arxiv.org/abs/2410.23132) | 3D adaptation of Spark framework |
-| [SimMIM](https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html) | Simple masked reconstruction |
-| [SwinUNETR SSL](https://arxiv.org/abs/2111.14791) | Transformer-based pre-training |
-| [SimCLR](https://arxiv.org/abs/2002.05709) | Contrastive learning baseline |
+| [Volume Contrastive (VoCo)](https://arxiv.org/abs/2402.17300) | Contrastive pretraining method for 3D volumes |
+| [VolumeFusion (VF)](https://arxiv.org/abs/2306.16925)         | Spatial volume fusion-based segmentation SSL method |
+| [Models Genesis (MG)](https://www.sciencedirect.com/science/article/pii/S1361841520302048) | Reconstruction and denoising based pretraining method |
+| [Masked Autoencoders (MAE)](https://openaccess.thecvf.com/content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper) | Default reconstruction based pretraining method |
+| [Spark 3D (S3D)](https://arxiv.org/abs/2410.23132) | Sparse reconstruction based pretraining mehtod (CNN only) |
+| [SimMIM](https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html) | Simple masked reconstruction based pretraining method (TR only) |
+| [SwinUNETR SSL](https://arxiv.org/abs/2111.14791) | Rotation, Contrastive and Reconstruction based pre-training method. |
+| [SimCLR](https://arxiv.org/abs/2002.05709) | Transfer of 2D Contrastive learning baseline method to 3D |