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New CT scan segmentation method uses weak supervision from text reports

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for segmenting CT scans using weak supervision derived from textual reports. This approach combines voxel-level supervision with slice-level classification loss extracted from scan-report pairs. By finetuning the SAM3 segmentation model, the technique significantly improves segmentation accuracy, showing a relative gain of up to 22% when using fewer fully labeled volumes. AI

IMPACT This research could lead to more efficient and scalable medical image analysis by reducing reliance on fully labeled datasets.

RANK_REASON The cluster contains an academic paper detailing a new method for CT volume segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CT scan segmentation method uses weak supervision from text reports

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The cluster contains an academic paper detailing a new method for CT volume segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sanjay Subramanian, Junwei Yu, Zirui Wang, Rohil Malpani, Maggie Chung, Adam Yala, Dan Klein, Trevor Darrell ·

    Open-Ended CT Volume Segmentation with Weak Supervision from Language

    arXiv:2607.25860v1 Announce Type: new Abstract: We introduce a method for training a text-conditioned segmentation model for CT scans, which combines voxel-level supervision with coarse but scalable slice-level supervision from reports. We extract, from a large database of scan-r…

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