The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Open accessAbstract
An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6× faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.
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Language Models are Few-Shot Learners
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“Recently, transformer based models [7, 83, 21, 64, 6] are emerged, which can capture the global relationship between whole volume.”
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SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.15161 · Cited by 115
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Self-citationZUMA: Training-Free Zero-Shot Unified Multimodal Anomaly Detection
Ma, Y., Liu, M., Jiang, S., Zhou, J., Bian, Y., Wang, X., & Wang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3658856 · Cited by 4
Metadata
- Authors
- Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z.
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Volume
- 2023
- Pages
- 21095-21107
- OpenAlex
- W4390889814
- Cited by
- 333
- References
- 95
- FWCI
- 37.86
- Type
- conference paper
- Language
- en
- Open access
- green
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