radiomics
PulseAugur coverage of radiomics — every cluster mentioning radiomics across labs, papers, and developer communities, ranked by signal.
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Foundation models show promise in cancer prediction but face generalization challenges
Researchers are exploring the use of foundation models for predicting head and neck cancer recurrence, comparing their performance against traditional radiomics and deep learning methods. One study found that a foundati…
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Glioblastoma radiomics study questions feature robustness for survival prediction
A new study published on arXiv investigated the relationship between the robustness of radiomic features and their predictive utility in glioblastoma (GBM) survival modeling. Researchers analyzed 4,752 radiomic features…
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ConRad framework enhances conformal prediction for medical radiomics
Researchers have developed ConRad, a new framework for conformal prediction in radiomics that aims to improve the efficiency and reliability of measurements derived from medical images. ConRad addresses the issue of ove…
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Deep learning aids radiomic feature selection for lung cancer detection
Researchers have developed a new framework called Gradient-Loss Recursive Feature Elimination (GL-RFE) to improve the selection of radiomic features for lung cancer stage detection. This method uses a deep neural networ…
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Medical foundation models lag behind radiomics for renal lesion CT analysis
A new benchmark study evaluated the effectiveness of three medical foundation models (FMs) for stratifying renal lesions in CT scans. While FMs showed promise by matching the performance of a 3D ResNet trained from scra…
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New multi-view VAE framework improves glioblastoma MRI radiomics prediction
Researchers have developed a novel multi-view latent representation learning framework using variational autoencoders (VAEs) to predict MGMT promoter methylation status in glioblastoma from MRI scans. This approach pres…