arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.
arXiv:2609.13665v1 Announce Type: new Abstract: Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolve...
Clear cell renal cell carcinoma (CCRCC) grading is essential for treatment planning, yet existing approaches either analyze patch-level images directly or focus solely on nuclei-level classification,...
The paper introduces a semantic‑guided multimodal preprocessing technique that fuses nuclei classification maps with RGB histopathology images for Vision Transformer‑based grading of clear cell renal cell carcinoma. By concatenating classification map channels and applying multiplicative modulation, the method achieves a balanced accuracy of 0.916, markedly surpassing an RGB‑only baseline (0.707) and prior max‑voting approaches (0.427). Sensitivity analysis shows the 21‑percentage‑point improvement remains robust under simulated perturbations matching current nuclei classifier error rates, indicating effective use of imperfect nuclear‑level information.
arXiv:2606. 17702v1 Announce Type: cross Abstract: Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extraction, and interpretable clinical reporting.
arXiv:2609.07313v1 Announce Type: new Abstract: Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of preci...
arXiv:2606. 12346v1 Announce Type: cross Abstract: Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remains a central challenge in computational pathology.
arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.
arXiv:2601.03163v2 Announce Type: replace Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet...
arXiv:2609.21698v1 Announce Type: new Abstract: Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive...
CellPath-Bench is a new benchmark that evaluates whole-slide cellular representations in pathology foundation models (PFMs) by using 25 spatially aligned H&E–Xenium tissue sections from 11 organs and over 7 million cells. It introduces metrics such as Cell Representation Advantage (CRA) and Cell Representation Transferability (CRT) to assess how well frozen PFMs encode cell-type information and generalize across tissue sections, datasets, and organs. The benchmark was applied to 30 PFMs, revealing significant model-dependent differences in cell-type decodability and cross-domain generalization, and offers a standardized framework for auditing cellular information in frozen PFM representations.
The paper introduces NoRDeC, a one‑class anomaly detection framework that uses a frozen residual U‑Net backbone pretrained on normal renal morphology to identify and characterize glomerular abnormalities. By combining Mahalanobis normal‑reference scoring with layer‑wise representation analysis and centered kernel alignment, NoRDeC achieves high AUROC scores across multiple abnormality categories, outperforming existing methods such as PaDiM and PatchCore. The study demonstrates that a model trained solely on normal glomeruli can detect unseen pathological subtypes and reveal subtype‑dependent changes in inter‑layer relationships without requiring abnormal training examples.