ArticleProceedings. IEEE International Conference on Computer Vision2023
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection.
Article in Proceedings. IEEE International Conference on Computer Vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.Biomedical engineering online · 2024Pooled it
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- NeuroSPFNet: Vision-Language Semantic Prior-Guided Frequency-Enhanced Network for Brain Tumor Segmentation in Multimodal MRI.Sensors (Basel, Switzerland) · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- OpenVocabCT: Toward Universal Text-Driven CT Image Segmentation.IEEE transactions on medical imaging · 2026Article
- LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- CMAF-Net: cross-modal attention fusion with information-theoretic regularization for imbalanced breast cancer histopathology.Scientific reports · 2026Article
- Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.eGastroenterology · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- A lung CT vision foundation model facilitating disease diagnosis and medical imaging.Nature communications · 2025Article
- Incorporating physicians' contouring style into auto-segmentation of clinical target volume for post-operative prostate cancer radiotherapy using a language encoder.Machine learning. Health · 2025Article
- Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2025Article
- Modality-projection universal model for comprehensive full-body medical imaging segmentation.Nature communications · 2025Article
- Feasibility Study of CLIP-Based Key Slice Selection in CT Images and Performance Enhancement via Lesion- and Organ-Aware Fine-Tuning.Bioengineering (Basel, Switzerland) · 2025Article
- Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification.Quantitative imaging in medicine and surgery · 2025Article
- DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and Test-Time Adaptation.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.