arXiv AI
Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints
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Towards Reliable AI-Based Histological Staining: A Systematic Study of Scaling and Uncertainty in Unpaired Generative Models
The paper presents a systematic evaluation of six unsupervised image‑to‑image generative models for virtual Sirius Red staining of mouse liver tissue from routine H&E slides. It benchmarks these models across 54 scaling configurations on a newly released paired H&E‑to‑SR dataset, assessing perceptual, distributional, and task‑specific performance, and further trains the best models into deep ensembles to quantify epistemic uncertainty. The study finds that GAN‑based and diffusion‑based methods differ markedly across these metrics, indicating that reliable virtual staining requires reporting and selecting on all three axes simultaneously.
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arXiv AI
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Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
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Through the Schr\"odinger Bridge: Benchmarking Antemortem Image Restoration from Postmortem Autolysis to Enhance Forensic Diagnostics
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arXiv AI
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arXiv Machine Learning
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