Pith. sign in

REVIEW

Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.01152 v1 pith:2D5IKXYE submitted 2023-12-02 eess.IV cs.CV

Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology

Sarah Cechnicka , Hadrien Reynaud , James Ball , Naomi Simmonds , Catherine Horsfield , Andrew Smith , Candice Roufosse , Bernhard Kainz This is my paper
classification eess.IV cs.CV
keywords diffusionimagescascadedhistopathologymodelmodelspfid-50ksynthesis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Diagnoses from histopathology images rely on information from both high and low resolutions of Whole Slide Images. Ultra-Resolution Cascaded Diffusion Models (URCDMs) allow for the synthesis of high-resolution images that are realistic at all magnification levels, focusing not only on fidelity but also on long-distance spatial coherency. Our model beats existing methods, improving the pFID-50k [2] score by 110.63 to 39.52 pFID-50k. Additionally, a human expert evaluation study was performed, reaching a weighted Mean Absolute Error (MAE) of 0.11 for the Lower Resolution Diffusion Models and a weighted MAE of 0.22 for the URCDM.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.