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New research explores multimodal AI for PET/CT lesion segmentation

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore multimodal self-supervised learning for PET/CT lesion segmentation in cancer patients. The first paper, MUST-PET, proposes a framework that uses both PET and CT scan data, trained across different radiotracers like FDG and PSMA, to improve generalization and reduce the need for extensive manual annotations. The second paper investigates various fusion strategies for combining data from two tracers, PSMA and FDG, finding that while fusion can be beneficial, tracer-specific models often perform better, especially when tracers capture complementary biological information for conditions like prostate cancer. AI

IMPACT These studies advance multimodal AI techniques for medical imaging, potentially improving cancer diagnosis and treatment planning by enhancing lesion segmentation accuracy and generalization.

RANK_REASON Two academic papers published on arXiv detailing novel AI approaches for medical image segmentation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores multimodal AI for PET/CT lesion segmentation

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle, Matthew E. Maeder, James B. Yu, Indrani Bhattacharya ·

    MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation

    arXiv:2608.19666v1 Announce Type: new Abstract: Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts. Self-supervised learning (SSL)…

  2. arXiv cs.CV TIER_1 English(EN) · Jack A. Johnson, Bart{\l}omiej W. Papie\.z ·

    When Two Tracers Disagree: An Investigation of Multimodal Fusion for Clinical PET/CT Segmentation

    arXiv:2608.19063v1 Announce Type: new Abstract: PSMA and FDG PET/CT visualise complementary biological information in prostate cancer. Combining both tracers could capture heterogeneous tumour phenotypes that may be missed by either alone, yet there is no consensus on effective d…

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