dataset

README.md

@@ -17,79 +17,69 @@ task_categories:
 
 ### Dataset Description
 
-This dataset contains **1,000 synthetic 3D chest CT scans** generated using the model introduced in
-[*From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation*](https://arxiv.org/abs/2506.00633) (Molino et al., BMVC 2026).
+This dataset contains **1,000 synthetic 3D chest CT scans** generated using the model introduced in  
+[*Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining*](https://arxiv.org/abs/2506.00633) (Molino et al., 2025).  
 
-The model was trained on the **CT-RATE dataset**, the largest publicly available collection of paired CT volumes and radiology reports.
+The model was trained on the **CT-RATE dataset**, the largest publicly available collection of paired CT volumes and radiology reports.  
 It leverages a **3D latent diffusion framework** combined with **contrastive vision-language pretraining (3D-CLIP)** to synthesize anatomically coherent and semantically faithful CT scans directly from clinical text prompts.
 
 These 1,000 scans were generated for the **VLM3D Challenge - Task 4**, serving as a benchmark resource for multimodal evaluation and synthetic data research in medical imaging.
 
-> **Note.** These scans were generated with the earlier version of the model ([arXiv v2](https://arxiv.org/abs/2506.00633v2)), the one submitted to the VLM3D Challenge.
-
-- **Curated by:** ArCo Lab – Università Campus Bio-Medico di Roma & Umeå University
-- **Language(s):** Conditioning reports are in English
+- **Curated by:** ArCo Lab – Università Campus Bio-Medico di Roma & Umeå University  
+- **Language(s):** Conditioning report are in English  
 - **License:** Apache 2.0
 
 ### Dataset Sources
 
-- **Repository:** [GitHub Repository](https://github.com/cosbidev/Text2CT)
-- **Paper:** [arXiv:2506.00633](https://arxiv.org/abs/2506.00633)
-- **Challenge:** [VLM3D Challenge](https://vlm3dchallenge.com)
+- **Repository:** [GitHub Repository](https://github.com/cosbidev/Text2CT)  
+- **Paper:** [arXiv:2506.00633](https://arxiv.org/abs/2506.00633)  
+- **Challenge:** [VLM3D Challenge](https://vlm3dchallenge.com)  
 
 ## Uses
 
 ### Direct Use
-
 - Benchmarking text-to-CT generative models.
 - Data augmentation for classification, detection, or segmentation tasks.
 - Research in multimodal vision-language learning for 3D medical imaging.
 - Educational purposes and simulation in medical training.
 
 ### Out-of-Scope Use
-
-- Direct diagnostic or clinical use.
-- Deployment in healthcare without proper validation and regulatory approval.
+- Direct diagnostic or clinical use.  
+- Deployment in healthcare without proper validation and regulatory approval.  
 - Any attempt to re-identify patients (note: scans are fully synthetic).
 
 ## Dataset Structure
 
-- **Format:** Volumetric CT scans stored in NIfTI (`.nii.gz`) format.
-- **Resolution:** Resampled to 0.75 × 0.75 × 3.0 mm voxel spacing, cropped/padded to 512 × 512 × 128.
-- **Intensity:** Normalized in Hounsfield Units (clipped to [−1000, +1000]).
-- **Content:** Synthetic chest CT scans across 18 pathological conditions (e.g., nodules, opacities, effusion, emphysema).
+- **Format:** Volumetric CT scans stored in NIfTI (`.nii.gz`) format.  
+- **Resolution:** Resampled to 0.75 × 0.75 × 3.0 mm voxel spacing, cropped/padded to 512 × 512 × 128.  
+- **Intensity:** Normalized in Hounsfield Units (clipped to [−1000, +1000]).  
+- **Content:** Synthetic chest CT scans across 18 pathological conditions (e.g., nodules, opacities, effusion, emphysema).  
 
 ## Dataset Creation
 
 ### Curation Rationale
-
 Created to provide a reproducible benchmark for **text-to-CT generation** and to supply **synthetic volumetric data** for research in data augmentation, privacy preservation, and multimodal foundation models.
 
 ### Source Data
-
 - Trained on **CT-RATE** (Hamamci et al., 2024), a large-scale dataset of chest CTs paired with radiology reports.
 
 ### Annotations
-
 No manual annotations included; diagnostic semantics are embedded via the conditioning text prompts used during generation.
 
 ### Personal and Sensitive Information
-
-- The dataset contains **no real patient data**.
+- The dataset contains **no real patient data**.  
 - All scans are **synthetic** and generated by a model trained on anonymized public datasets.
 
 ## Bias, Risks, and Limitations
 
-- Synthetic data may not fully capture rare pathologies or distributional nuances of real-world scans.
-- While useful for augmentation and benchmarking, these scans are **not clinically validated**.
+- Synthetic data may not fully capture rare pathologies or distributional nuances of real-world scans.  
+- While useful for augmentation and benchmarking, these scans are **not clinically validated**.  
 - There is a potential risk if synthetic data are used without acknowledging their limitations in medical research.
 
 ### Recommendations
-
-Users should:
-
-- Combine synthetic with real-world data for downstream tasks.
-- Avoid over-relying on synthetic volumes for clinical translation.
+Users should:  
+- Combine synthetic with real-world data for downstream tasks.  
+- Avoid over-relying on synthetic volumes for clinical translation.  
 - Report the provenance of synthetic data when used in publications.
 
 ## Citation
@@ -97,14 +87,11 @@ Users should:
 If you use this dataset, please cite the following work:
 
 **BibTeX:**
-
-```bibtex@misc{molino2026alignment,
-      title={From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation}, 
-      author={Daniele Molino and Camillo Maria Caruso and Filippo Ruffini and Paolo Soda and Valerio Guarrasi},
-      year={2026},
-      eprint={2506.00633},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2506.00633}, 
+```bibtex
+@article{molino2025textct,
+  title={Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining},
+  author={Molino, Daniele and Caruso, Camillo Maria and Ruffini, Filippo and Soda, Paolo and Guarrasi, Valerio},
+  journal={arXiv preprint arXiv:2506.00633},
+  year={2025}
 }
 ```