PET-CT
PulseAugur coverage of PET-CT — every cluster mentioning PET-CT across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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AI predicts lung cancer mutations from scans using multi-label learning
Researchers have developed a multi-label learning approach using deep learning to predict specific gene mutations (EGFR, TP53, and KRAS) in non-small cell lung cancer (NSCLC) from PET/CT scans. The study, conducted on a…
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New benchmarks and methods advance medical vision-language models
Researchers have developed new benchmarks and distillation techniques to improve the capabilities of vision-language models (VLMs) in the medical domain. PathAgentBench focuses on evaluating VLMs' ability to acquire and…
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LLMs and RL enhance PET/CT lesion segmentation in new RADIANT-PET framework
Researchers have developed RADIANT-PET, a novel framework for improving lesion segmentation in PET/CT scans for oncology. This system integrates a voxel-level segmentation model with a large language model (LLM) for les…
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HECKTOR 2025 challenge benchmarks AI for head and neck cancer analysis · 2 sources tracked
The HECKTOR 2025 challenge, building on previous iterations, established a benchmark for automated head and neck cancer analysis using multimodal PET/CT imaging and electronic health records. This challenge involved ove…
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New MuDuo framework uses dual-foundation models for semi-supervised PET/CT segmentation
Researchers have developed a novel semi-supervised learning framework called MuDuo for segmenting organs in PET/CT scans. This method leverages dual-foundation models, utilizing SAM-Med3D for CT imaging and SegAnyPET fo…
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AI framework MuDuo enhances PET/CT segmentation with dual-foundation models
Researchers have developed a novel mutual distillation framework called MuDuo for semi-supervised segmentation of PET/CT scans, addressing the high cost of manual annotation in oncology. This framework leverages dual-fo…
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AI models predict lung cancer survival from PET/CT scans
Researchers have developed new AI models, ATCS and MTS, to predict overall survival in lung cancer patients using PET/CT scans. These models outperformed a baseline TCS model, achieving AUCs of 0.794 and 0.793 respectiv…
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AI models struggle with unseen PET/CT tracer combinations despite segmentation gains
The autoPET3 challenge, held in conjunction with MICCAI 2024, focused on automated lesion segmentation in whole-body PET/CT scans, specifically testing compositional generalization. The challenge utilized a large datase…