Virchow2: Scaling self-supervised mixed magnification models in pathology
Foundation models are rapidly being developed for computational pathology applications.
However, it remains an open question which factors are most important for downstream …
However, it remains an open question which factors are most important for downstream …
[HTML][HTML] Towards generative digital twins in biomedical research
J Wu, VH Koelzer - Computational and Structural Biotechnology Journal, 2024 - Elsevier
Digital twins in biomedical research, ie virtual replicas of biological entities such as cells,
organs, or entire organisms, hold great potential to advance personalized healthcare. As all …
organs, or entire organisms, hold great potential to advance personalized healthcare. As all …
Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images
Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung
cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are …
cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are …
Review of deep learning-based pathological image classification: From task-specific models to foundation models
H Luan, K Yang, T Hu, J Hu, S Liu, R Li, J He… - Future Generation …, 2025 - Elsevier
Pathological diagnosis is considered the gold standard in cancer diagnosis, playing a
crucial role in guiding treatment decisions and prognosis assessment for patients. However …
crucial role in guiding treatment decisions and prognosis assessment for patients. However …
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression
within tissue, enabling detailed study of cellular heterogeneity and tissue organization …
within tissue, enabling detailed study of cellular heterogeneity and tissue organization …
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
Spatial transcriptomics (ST) provides high-resolution pathological images and whole-
transcriptomic expression profiles at individual spots across whole-slide scales. This setting …
transcriptomic expression profiles at individual spots across whole-slide scales. This setting …
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs)
offering valuable insights that directly influence clinical decision-making. However, the large …
offering valuable insights that directly influence clinical decision-making. However, the large …
[HTML][HTML] Explainable AI for computational pathology identifies model limitations and tissue biomarkers
Deep learning models have shown promise in histopathology image analysis, but their
opaque decision-making process poses challenges in high-risk medical scenarios. Here we …
opaque decision-making process poses challenges in high-risk medical scenarios. Here we …
PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation model
Tumor morphological features from histology images are a cornerstone of clinical pathology,
diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which …
diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which …
A deep learning-based multiscale integration of spatial omics with tumor morphology.
B Schmauch, L Herpin, A Olivier, T Duboudin… - bioRxiv, 2024 - biorxiv.org
Spatial Transcriptomics (spTx) offers unprecedented insights into the spatial arrangement of
the tumor microenvironment, tumor initiation/progression and identification of new …
the tumor microenvironment, tumor initiation/progression and identification of new …