arXiv:2606. 24430v1 Announce Type: cross Abstract: Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and improves performance.
arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.
arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...
This study presents a clinically relevant framework for evaluating deep neural networks that segment lymphoma lesions in PET/CT images, addressing gaps such as out‑of‑distribution testing and comparison with expert annotators. Using 611 multi‑institutional cases, the authors assess four networks (ResUNet, SegResNet, DynUNet, SwinUNETR) with lesion‑specific metrics, detection criteria, and metabolic‑characteristic‑based thresholds, finding that models perform best on large, intense lesions. The work also demonstrates that network errors mirror those of physicians, highlighting shared challenges with small, faint lesions.
arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understanding, limiting their sensitivity to fine-graine...
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: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.
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing...
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:2609.32876v2 Announce Type: replace-cross Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons...
arXiv:2608. 07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods for vision transformer...