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sequencelengths 2.05k
2.05k
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sequencelengths 46
46
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End of preview. Expand
in Data Studio
MedPointS-CLS
This is the medical point cloud classification dataset from MedPointS, where data is input point cloud, and label is the class label.
Each point cloud has been normalized and sub-sampled to 2048 points. The correspondence between class names and labels is listed as follows (the label value plus 1 is the actual key of following map):
coarse_label_to_organ = {1: 'adrenalgland',
2: 'aorta',
3: 'autochthon',
4: 'bladder',
5: 'brain',
6: 'breast',
7: 'bronchie',
8: 'celiactrunk',
9: 'cheek',
10: 'clavicle',
11: 'colon',
12: 'costa',
13: 'duodenum',
14: 'esophagus',
15: 'eyeball',
16: 'femur',
17: 'gallbladder',
18: 'gluteusmaximus',
19: 'heart',
20: 'hip',
21: 'humerus',
22: 'iliacartery',
23: 'iliacvena',
24: 'iliopsoas',
25: 'inferiorvenacava',
26: 'kidney',
27: 'liver',
28: 'lung',
29: 'mediastinaltissue',
30: 'pancreas',
31: 'portalveinandsplenicvein',
32: 'smallbowel',
33: 'spleen',
34: 'stomach',
35: 'thymus',
36: 'thyroid',
37: 'trachea',
38: 'uterocervix',
39: 'uterus',
40: 'vertebrae',
41: 'gonads',
42: 'sacrum',
43: 'clavicula',
# 44: 'prostate',
44: 'pulmonaryartery',
# 45: 'ribcartilage',
45: 'rib',
46: 'scapula',
# 48: 'skull',
# 49: 'spinalcanal',
# 50: 'sternum'
}
If you find our project helpful, please consider to cite the following works:
@misc{zhang2025hierarchicalfeaturelearningmedical,
title={Hierarchical Feature Learning for Medical Point Clouds via State Space Model},
author={Guoqing Zhang and Jingyun Yang and Yang Li},
year={2025},
eprint={2504.13015},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.13015},
}
dataset_info: features: - name: data sequence: sequence: float32 - name: label sequence: float32 splits: - name: train num_bytes: 947171520 num_examples: 28737 download_size: 718817756 dataset_size: 947171520 configs: - config_name: default data_files: - split: train path: data/train-*
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