RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
arXiv:2606.28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal. We present RADIANT-PET, a reasoning-augmented framework that couples a high-sensitivity voxel-level segmentation model with lesion-level large language model (LLM) adjudication. Candidate uptake regions are generated with a deliberately permissive segmentation stage, then converted into structured textual descriptions that summarize uptake intensity, morphology, and regional and glo
The increasing sophistication of large language models and advancements in medical imaging AI convergence are enabling more nuanced and accurate diagnostic tools.
This development indicates a growing capability for AI to perform intricate reasoning in critical diagnostic tasks, potentially transforming medical imaging and oncology.
The diagnostic process for PET/CT scans could become significantly more accurate and automated, reducing misdiagnosis rates and improving patient outcomes through early detection.
- · Oncologists
- · Medical AI developers
- · Diagnostic imaging centers
- · Cancer patients
- · Legacy medical imaging software
- · Human-only pathology review
Improved accuracy in PET/CT lesion segmentation leads to earlier and more precise cancer diagnoses.
The integration of LLMs with imaging AI could create a paradigm shift in diagnostic medical AI, leading to widespread adoption across various medical specialties.
Reduced healthcare costs due to more efficient diagnostics and potentially earlier, less invasive treatments.
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Read at arXiv cs.LG