Stain-Aware Wavelet Regularization (SAWR) is an adversarial purification framework designed for histopathology images. It uses multi-level Haar wavelet-domain regularization to separate adversarial noise from diagnostically relevant tissue structures, applying stain-specific frequency constraints for Hematoxylin and Eosin channels. Experiments show SAWR improves adversarial robustness by up to 10.69% while preserving texture and spectral fidelity.
arXiv:2606. 07222v1 Announce Type: cross Abstract: Cell detection in histopathology images strongly depends on surrounding tissue context, where visually similar cells may belong to different classes under different microenvironments.
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:2607. 04401v1 Announce Type: cross Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached?
arXiv:2608.29456v1 Announce Type: new Abstract: As artificial intelligence is increasingly integrated into chest X-ray (CXR) interpretation, triage, and clinical decision support, understanding its v...
arXiv:2601. 08127v2 Announce Type: replace-cross Abstract: Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens of cases may be unavailable.
The paper introduces "Destroy Me", a hybrid framework that generates realistic histopathology artifacts using Stable Diffusion and physics‑based modeling to create six common artifact types. Artifact realism is evaluated with KID and color Wasserstein metrics, and models trained on these augmented images outperform baselines on lung adenocarcinoma classification, achieving a 10.5% relative boost in macro F1‑score and a 15% increase in Cohen’s Kappa. The study highlights that selective, impact‑weighted augmentation is essential for enhancing robustness while preserving subtle diagnostic features.
arXiv:2607. 21343v1 Announce Type: cross Abstract: Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding.
arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privac...
EXPOSE is a framework that applies Sparse Autoencoders to Vision Foundation Model embeddings in computational pathology, aiming to separate biological signals from domain‑specific noise. By training a sparse representation of VFM features and using a linear classifier to flag domain‑specific latent dimensions, the method masks these components before downstream relapse prediction, avoiding the need to retrain the backbone model. Experiments on a large prostate cancer dataset demonstrate that removing domain‑specific features improves cross‑domain performance and raises the Domain Robustness Index (DoRI).
arXiv:2601.17228v2 Announce Type: replace Abstract: Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches e...
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
arXiv:2407. 13632v2 Announce Type: replace-cross Abstract: Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning.