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Natural language instructions can now drive zero-shot 3D organoid phenotyping by fusing Cellpose geometric priors with SAM3 inside an autonomous multi-agent framework.
Generating realistic patient embeddings can yield performance on par with full datasets, even in scenarios with missing classes.
MAE-3D not only surpasses 2D methods in single-cell tasks but also sets new benchmarks for protein localization and interaction, showcasing the power of 3D modeling in microscopy.
QG-MIL achieves a remarkable +6.1 mean macro F1 point improvement across diverse medical imaging tasks, redefining stability and performance in attention-based learning.
