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 scratch and requiring significantly less computational power, they did not surpass a traditional radiomics classifier. The study suggests that current generalist FM embeddings may not yet capture the detailed textural and shape variations crucial for distinguishing lesion subtypes, leaving radiomics as the current state-of-the-art for this specific task. AI

影响 Foundation models show potential for medical imaging analysis but currently do not outperform established radiomics methods for renal lesion stratification.

排序理由 The cluster contains an academic paper detailing a benchmark study on medical foundation models. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• 3D ResNet-50
• CT scans
• Foundation Models
• Radiomics
• Renal Lesion Stratification
• The Cancer Imaging Archive

• 论文
• 其他

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