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.
arXiv AI
Sep 2
StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization
StainPresetNet is a new framework for stain normalization that combines structural preservation with dataset-level color mapping while remaining computationally efficient. It uses pixel-wise normalization guided by preset reference images, allowing multi-directional adaptability without retraining. Experiments on cytopathology and histopathology datasets show that it outperforms conventional methods in color mapping accuracy, improves classifier generalization, and cuts computational overhead by 90% compared to existing deep learning approaches.
arXiv AI
Jul 7
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
arXiv:2607. 03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance.
arXiv Computer Vision
Aug 25
Through the Schr\"odinger Bridge: Benchmarking Antemortem Image Restoration from Postmortem Autolysis to Enhance Forensic Diagnostics
arXiv:2608.21813v1 Announce Type: new Abstract: Forensic histopathology, essential for determining cause of death and disease diagnosis, is severely impeded by postmortem autolysis, i.e., an irrevers...
arXiv Computer Vision
Sep 30
HERO: Histology Encoder for Robust Representation in Oncology
HERO (Histology Encoder for Robust Representation in Oncology) is a ViT‑G/14 pathology foundation model trained with DINO and iBOT objectives and refined using high‑resolution Gram anchoring on a 500‑million‑tile corpus from about 575,000 clinical whole‑slide images. It demonstrates superior robustness to center, scanner, and stain variation compared to other state‑of‑the‑art foundation models, while maintaining competitive performance on tile‑level classification, segmentation, and gene‑expression prediction. Across 39 slide‑level clinical tasks, HERO ranks first on average and achieves the best average rank across six benchmark frameworks under an equal‑weighted analysis.
arXiv AI
Jun 8
DaX: Learning General Pathology Representations Across Scales
arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.