Lightweight contrastive alignment of frozen histopathology and RNA-seq foundation models enables open-vocabulary molecular prediction from H&E slides, reporting 25-fold retrieval gains on a 1720-sample multi-cancer cohort with clinical validation.
Molecular-driven foundation model for oncologic pathology
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 8verdicts
UNVERDICTED 8representative citing papers
PathLab is an agentic framework that translates natural-language objectives into validated computational pathology workflows, achieving non-inferior performance on 12 datasets across four task families while enabling non-programmers to conduct studies.
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
MixTIME uses a learnable-router MoE to fuse three pathology foundation models for pixel- and slide-level prediction of 17 mIF protein markers from H&E images, improving spatial domain ID, survival prediction, and pathologist-validated report generation.
AGE-MIL is a new MIL framework that uses patient-level anchors to direct patch selection and evidence accumulation for stable patient-level predictions on six pathology tasks, outperforming eight prior MIL methods.
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
An expert-guided contrastive fine-tuning framework improves fine-grained slide-level classification of pediatric brain tumors under low-sample and class-imbalanced conditions.
A two-stage training method using self-supervised pretraining on cell images followed by contrastive alignment with genetic data creates improved patient encoders for hematological diagnosis.
citing papers explorer
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Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction
Lightweight contrastive alignment of frozen histopathology and RNA-seq foundation models enables open-vocabulary molecular prediction from H&E slides, reporting 25-fold retrieval gains on a 1720-sample multi-cancer cohort with clinical validation.
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Democratizing and accelerating AI-driven pathology research through agentic intelligence
PathLab is an agentic framework that translates natural-language objectives into validated computational pathology workflows, achieving non-inferior performance on 12 datasets across four task families while enabling non-programmers to conduct studies.
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Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
GLMP generates robust pathology embeddings by routing histology images through an intermediate textual representation produced by general-purpose MLLMs to mitigate batch effects.
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Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
MixTIME uses a learnable-router MoE to fuse three pathology foundation models for pixel- and slide-level prediction of 17 mIF protein markers from H&E images, improving spatial domain ID, survival prediction, and pathologist-validated report generation.
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AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction
AGE-MIL is a new MIL framework that uses patient-level anchors to direct patch selection and evidence accumulation for stable patient-level predictions on six pathology tasks, outperforming eight prior MIL methods.
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Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
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Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
An expert-guided contrastive fine-tuning framework improves fine-grained slide-level classification of pediatric brain tumors under low-sample and class-imbalanced conditions.
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Genetically Aligned Patient Representations Improve Hematological Diagnosis
A two-stage training method using self-supervised pretraining on cell images followed by contrastive alignment with genetic data creates improved patient encoders for hematological diagnosis.