Improve dataset card: Add task categories, links, abstract, and sample usage (#1)

- Improve dataset card: Add task categories, links, abstract, and sample usage (44c993eaab35da507555380442dd98721680d74b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,13 +1,27 @@
 ---
-license: mit
 language:
 - en
+license: mit
 tags:
 - biology
 - point cloud
 - classification
 - medical
+- completion
+- segmentation
+task_categories:
+- graph-machine-learning
 ---
+
+This repository contains the `MedPointS-CLS` dataset, a large-scale medical point cloud dataset for anatomy classification, completion, and segmentation, as presented in the paper [Hierarchical Feature Learning for Medical Point Clouds via State Space Model](https://huggingface.co/papers/2504.13015).
+
+*   **Paper**: [Hierarchical Feature Learning for Medical Point Clouds via State Space Model](https://huggingface.co/papers/2504.13015)
+*   **Project Page**: https://flemme-docs.readthedocs.io/en/latest/medpoints.html
+*   **Code**: https://github.com/wlsdzyzl/flemme
+
+### Abstract
+Deep learning-based point cloud modeling has been widely investigated as an indispensable component of general shape analysis. Recently, transformer and state space model (SSM) have shown promising capacities in point cloud learning. However, limited research has been conducted on medical point clouds, which have great potential in disease diagnosis and treatment. This paper presents an SSM-based hierarchical feature learning framework for medical point cloud understanding. Specifically, we down-sample input into multiple levels through the farthest point sampling. At each level, we perform a series of k-nearest neighbor (KNN) queries to aggregate multi-scale structural information. To assist SSM in processing point clouds, we introduce coordinate-order and inside-out scanning strategies for efficient serialization of irregular points. Point features are calculated progressively from short neighbor sequences and long point sequences through vanilla and group Point SSM blocks, to capture both local patterns and long-range dependencies. To evaluate the proposed method, we build a large-scale medical point cloud dataset named MedPointS for anatomy classification, completion, and segmentation. Extensive experiments conducted on MedPointS demonstrate that our method achieves superior performance across all tasks. The dataset is available at this https URL . Code is merged to a public medical imaging platform: this https URL .
+
 ### MedPointS-CLS
 This is the medical point cloud classification dataset from [MedPointS](https://flemme-docs.readthedocs.io/en/latest/medpoints.html), where `data` is input point cloud, and `label` is the class label. 
 
@@ -68,6 +82,23 @@ coarse_label_to_organ = {1: 'adrenalgland',
     }
 ```
 
+### Sample Usage
+The associated code repository `flemme` provides clear instructions and configuration files for training and testing models on the MedPointS dataset for classification, completion, and segmentation tasks.
+
+To train and evaluate models, first set up the `flemme` environment (refer to the [Github README](https://github.com/wlsdzyzl/flemme) for installation instructions). Then, use the following commands:
+
+```bash
+## classification
+train_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cls/train_pointmamba2knn_clm.yaml
+test_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cls/test_pointmamba2knn_clm.yaml
+## completion
+train_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cpl/train_pointmamba2knn_cpl.yaml
+test_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cpl/test_pointmamba2knn_cpl.yaml
+## segmentation
+train_flemme --config /path/to/project/flemme/resources/pcd/medpoints/seg/train_pointmamba2knn_sem.yaml
+test_flemme --config /path/to/project/flemme/resources/pcd/medpoints/seg/test_pointmamba2knn_sem.yaml
+```
+
 If you find our project helpful, please consider to cite the following works:
 
 ```