CHIS lets pretrained diffusion models generate structurally controlled histopathology images without any training by frequency-domain initialization and wavelet-based textural modulation.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation
CHIS lets pretrained diffusion models generate structurally controlled histopathology images without any training by frequency-domain initialization and wavelet-based textural modulation.
-
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.