Lung cancer outcomes depend on early detection and accurate lesion delineation, yet conventional segmentation methods remain clinically detached by yielding scanner sensitive pixel masks that do not align with radiologist language or reporting standards. To address this limitation, we propose BiomedLoop, a text guided framework that integrates semantic descriptions with spatial quantification to mirror routine diagnostic practice. Our pipeline couples localization via fine-tuned Grounding DINO and refinement using SEEM, which is enhanced by a novel Uncertainty Aware Feature Modulator to ensure boundary sensitive representation. A core innovation involves converting mask derived geometric descriptors into structured pseudo text prompts to fine tune the localization pathway, enabling supervision even on datasets without native radiology reports. Additionally, the system outputs structured reports compliant with the TID 1500 specification. Extensive experiments across five public benchmarks demonstrate that BiomedLoop yields elevated Dice similarity coefficients and consistently lower Hausdorff distances relative to both conventional CNN architectures and Segment Anything Model variants. Collectively, these results show that systematic semantic spatial joint modeling successfully bridges the critical disconnect between traditional segmentation and clinical utility in resource limited settings.
Wang et al. (Thu,) studied this question.
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