Article

Adaptive Integration of Heterogeneous Foundation Models to Find Histologically Predictable Genes in Breast Cancer

2026-04-08

Abstract excerpt

Foundation models for computational pathology have rapidly emerged as powerful tools for extracting rich biological and morphological representations from histopathology images. However, variations in model architecture, pre-training data, and optimization objectives often lead to task-dependent performance, rather than universal generalization. As a result, effective strategies for integrating their complementary...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 216a4171-4831-51c7-8163-ec8a68da1a19
DOI: 10.64898/2026.04.05.716435

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Adaptive Integration of Heterogeneous Foundation Models to Find Histologically Predictable Genes in Breast CancerDOI 10.64898/2026.04.05.716435Read

Extended pre-training of histopathology foundation models uncovers co-existing breast cancer archetypes characterized by RNA splicing or TGF-β dysregulationSemantic proximity Read

Translating Histopathology Foundation Model Embeddings into Cellular and Molecular Features for Clinical StudiesSemantic proximity Read

Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation ModelSemantic proximity Read

Explainable Prototype Booster: Enhancing Latent Representations of Foundation Models for Gene Expression PredictionSemantic proximity Read

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image.Semantic proximity Read

Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypesSemantic proximity Read

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation modelSemantic proximity Read

Multi-Modal Foundation Model with Whole-Slide Attention Enables Transferrable Digital Pathology at Single-Cell ResolutionSemantic proximity Read

Assessing Foundation Models for Computational Pathology in Endometrial CancerSemantic proximity Read

HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision OncologySemantic proximity Read

STPath: A Generative Foundation Model for Integrating Spatial Transcriptomics and Whole Slide ImagesSemantic proximity Read

BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer DiagnosisSemantic proximity Read