The paper proposes a scalable framework that uses cell-level spatial transcriptomics (ST) as weak supervision for nuclei classification in histopathology images. By converting gene expression profiles into cell-type labels and leveraging nucleus masks from ST data, the method automatically generates image-label pairs without manual annotation. Evaluations on unseen tissues show that this approach achieves segmentation and classification performance comparable to conventional supervised methods while requiring fewer manually annotated datasets.
arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
arXiv:2606. 08712v1 Announce Type: cross Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context.
The paper introduces BCARP, a Biology‑Guided Context‑Aware Residual Propagation framework that predicts spatial gene expression from H&E‑stained histology images. Rather than directly decoding expression from visual features, BCARP treats histology embeddings as morphological priors and iteratively refines spot representations in a transcriptomics‑oriented latent space, integrating nuclei‑guided local context and prototype‑guided global propagation. Evaluations on three spatial transcriptomics datasets and ten HEST‑1k cohorts show that BCARP outperforms existing baselines in mean correlation‑based metrics, demonstrating the effectiveness of biology‑guided local‑global residual propagation for histology‑to‑transcriptomics prediction.
arXiv:2609.18578v1 Announce Type: new Abstract: Pathologists diagnose diseases by first locating suspicious tissue and then examining it at higher magnification, whereas self-supervised vision transf...
GATE-ST is a gene-aware text-image encoder that enhances spatial transcriptomics predictions by integrating gene descriptions into image-based models. The method encodes gene summaries with a text encoder and fuses these embeddings with image features via cross‑attention, aligning them with morphological cues. Benchmarks show GATE‑ST outperforms random gene embeddings and other image‑text fusion architectures, indicating its potential to improve accuracy while reducing time and cost in spatial gene expression analysis.
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.
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).
arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,...
arXiv:2607. 10783v1 Announce Type: cross Abstract: Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive.
arXiv:2610.31902v1 Announce Type: new Abstract: Objectives: In the area of digital pathology, nucleus segmentation is an essential step for quantitative analysis; however, high nuclear density and st...
arXiv:2610.26988v1 Announce Type: new Abstract: Histopathology is central to cancer diagnosis, but morphology in Hematoxylin and Eosin (HE)-stained images does not directly reveal the molecular state...
arXiv:2603. 13377v2 Announce Type: replace-cross Abstract: Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features.