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A hybrid approach that draws on the stability and generation quality of denoising diffusion models, which yields a stable training process and generates microstructures of varying complexity with minimal slice artefacts and close agreement with ground-truth phase fractions and structural descriptors.
SlideCRF achieves a remarkable +37.5% improvement in predictive accuracy with just 16 clicks, transforming how pathologists can interact with AI in cancer diagnostics.
