Researchers have developed LETT-NeXt, a lightweight model designed for 3D lesion segmentation in CT scans, guided by RECIST markers. This model aims to provide a more comprehensive 3D description of lesion extent compared to traditional 2D RECIST diameter measurements. LETT-NeXt integrates RECIST information as prompt channels and utilizes a MedNeXt-v2 architecture for prediction, achieving competitive results in the CVPR 2026 Foundation Models for Pan-cancer Segmentation in CT Images competition. AI
IMPACT This model offers improved 3D lesion segmentation for cancer assessment, potentially aiding in more accurate treatment response evaluation.
RANK_REASON The item describes a new model and its performance on a specific task, presented in the context of a competition. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Hugging Face Daily Papers →
- 3d Ct
- CVPR 2026 Foundation Models for Pan-cancer Segmentation in CT Images
- Lesion Segmentation
- LETT-NeXt
- Response Evaluation Criteria in Solid Tumors
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