Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations

Q: What does this research mean for the field?

Deep learning models can achieve better or comparable performance in mediastinal lymph node assessment on PET/CT images without the need for pixel-level annotations. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Sofija Engelson; Yannic Elser; Malte Maria Sieren; Jan Ehrhardt; Julia Andresen; Stefanie Schierholz; Tobias Keck; Daniel Drömann; Jörg Barkhausen; H. Handels

doi:10.1117/1.jmi.13.1.014507

February 18, 2026Journal of Medical Imaging

Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations

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Key Points

The aim is to evaluate mediastinal lymph node assessment using deep learning without needing detailed pixel-level annotations.
Developed models using deep learning techniques.
Divided the assessment problem into subtasks for better management.
Integrated prior knowledge to enhance model performance.
Models showed improved or comparable performance without pixel-level annotations.
Integration of prior knowledge led to significant enhancements in assessment accuracy.

Implication

Deep learning models assess mediastinal lymph nodes on PET/CT images, indicating improved performance with prior knowledge integration.

Abstract

The division of the problem setting into subtasks as well as the integration of prior knowledge enables better or comparable performance of models trained with and without segmentation masks.

Discussion

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Cite This Study

Engelson et al. (2026) studied this question.

synapsesocial.com/papers/699a9ca1482488d673cd2589 https://doi.org/10.1117/1.jmi.13.1.014507

View at Publisher

Ask AI

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Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations

Key Points

Implication

Abstract

Discussion

Cite This Study

Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations

Key Points

Implication

Abstract

Discussion

Cite This Study

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