chn123/curia2-nlst

Curia-2: Slice-Level Deep Representations & Attention-MIL Survival Modeling on NLST

This repository hosts the Curia-2 axial slice-level 768-dimensional deep feature representations and longitudinal risk modeling framework evaluated across the National Lung Screening Trial (NLST) cohort (2,965 LDCT series from 1,201 independent screening patients).

🔬 Model Architecture & Pipeline

• Slice-Level Backbone: Curia-2 foundation model visual feature representations per axial slice ($N_i \times 768$).
• Positional Encoding: Sinusoidal Z-positional encoding modeling relative anatomical depth (Lung Apex $\rightarrow$ Base).
• Aggregator: Gated Attention Multiple Instance Learning (Attention-MIL) with dynamic padding masks.
• Head: Discrete-Time Hazard Survival Head predicting cumulative annual lung cancer risk ($Y_1 \dots Y_6$).

📁 Repository Structure

• multi_series_embeddings.parquet: Compact columnar storage of slice embeddings, metadata, patient IDs, follow-up times, and survival outcomes.

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📖 References & Citation

If you use Curia-2 representations, pre-trained weights, or the Attention-MIL pipeline, please cite:

@misc{saporta2026curia2scalingselfsupervisedlearning,
  title         = {Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models}, 
  author        = {Antoine Saporta and Baptiste Callard and Corentin Dancette and Julien Khlaut and Charles Corbi{\`e}re and Leo Butsanets and Amaury Prat and Pierre Manceron},
  year          = {2026},
  eprint        = {2604.01987},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2604.01987}
}

@article{nlst2011reduced,
  title   = {Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening},
  author  = {{National Lung Screening Trial Research Team}},
  journal = {New England Journal of Medicine},
  volume  = {365},
  number  = {5},
  pages   = {395--409},
  year    = {2011}
}

@article{mikhael2023sybil,
  title   = {Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography},
  author  = {Mikhael, Peter G. and Wohlwend, Jeremy and Yala, Adam and Karstens, Leslie and Xiang, Jing and Takigami, Amanda K. and others},
  journal = {Journal of Clinical Oncology},
  volume  = {41},
  number  = {12},
  pages   = {2191--2201},
  year    = {2023}
}

@misc{agrawal2025pillar0,
  title         = {Pillar-0: A New Frontier for Radiology Foundation Models}, 
  author        = {Kumar Krishna Agrawal and Longchao Liu and Long Lian and Michael Nercessian and Natalia Harguindeguy and Yufu Wu and Peter Mikhael and Gigin Lin and Lecia V. Sequist and Florian Fintelmann and Trevor Darrell and Yutong Bai and Maggie Chung and Adam Yala},
  year          = {2025},
  eprint        = {2511.17803},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2511.17803}
},
  author  = {Pillar Research Team and Collaborators},
  journal = {arXiv preprint arXiv:2511.17803},
  year    = {2025}
}