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eess.IV2025
Can General-Purpose Omnimodels Compete with Specialists? A Case Study in Medical Image Segmentation
Yizhe Zhang, Qiang Chen, Tao Zhou
The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform…
eess.IV2024
Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
Ahjol Senbi, Tianyu Huang, Fei Lyu +8
We explore the feasibility and potential of building a ground-truth-free evaluation model to assess the quality of segmentations generated by the Segment Anything Model (SAM) and i…