RT-Super is a longitudinal, multimodal algorithm for multi-tumor segmentation that learns from radiology reports, with code already available and a forthcoming README.

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Last updated

2026-06-29

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FR-AI / ANALYSIS

Why it is worth attention

It introduces a novel approach combining longitudinal imaging, multiple modalities, and report-derived learning for multi-tumor segmentation, accepted at MICCAI 2026.

Who it is for

  • Medical image analysis researchers
  • Radiologists interested in automated tumor tracking
  • AI practitioners in healthcare
  • Clinical data scientists

Use cases

  • Longitudinal monitoring of tumor burden across time
  • Multi-tumor segmentation from combined MRI/CT and text reports
  • Training segmentation models using only radiology reports
  • Benchmarking report-based learning for medical imaging

Strengths

  • Code is already publicly available for immediate use
  • Addresses an underexplored problem – learning from reports rather than manual annotations
  • Multimodal and longitudinal design aligns with clinical practice
  • Accepted at a top venue (MICCAI 2026)

Considerations

  • No performance metrics or comparisons provided in the README
  • Documentation is minimal with 'README coming soon'
  • Reproducibility details (dependencies, training procedure) are absent

README quick start

RT-Super

[MICCAI 2026] A longitudinal, multimodal algorithm for multi-tumor segmentation (learning from reports).

Code already available, README coming soon!

Description

[MICCAI 2026] A longitudinal, multimodal algorithm for multi-tumor segmentation (learning from reports).

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