MMA is a threshold-free continuous metric for instance segmentation that uses globally optimal bipartite matching between predictions and ground truth followed by per-pixel normalization to aggregate overlap.
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CHIS steers pretrained diffusion models to generate histopathology images aligned with input structural masks via frequency-domain structural initialization and wavelet-based textural modulation without any training on annotated data.
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Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching
MMA is a threshold-free continuous metric for instance segmentation that uses globally optimal bipartite matching between predictions and ground truth followed by per-pixel normalization to aggregate overlap.
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Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation
CHIS steers pretrained diffusion models to generate histopathology images aligned with input structural masks via frequency-domain structural initialization and wavelet-based textural modulation without any training on annotated data.