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arXiv · 2603.07895

MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models

Minsoo Lee·Jonghyun Kim·Juseung Yun·Sunwoo Yu·Jongseong Jang
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Abstract

Pathology foundation models learn morphological representations through self-supervised pretraining on large-scale whole-slide images, yet they do not explicitly capture the underlying molecular state of the tissue. Spatial transcriptomics technologies bridge this gap by measuring gene expression in situ, offering a natural cross-modal supervisory signal. We propose MINT (Molecularly Informed Training), a fine-tuning framework that incorporates spatial transcriptomics supervision into pretrained pathology Vision Transformers. MINT appends a learnable ST token to the ViT input to encode transcriptomic information separately from the morphological CLS token, preventing catastrophic forgetting through DINO self-distillation and explicit feature anchoring to the frozen pretrained encoder. Gene expression regression at both spot-level (Visium) and patch-level (Xenium) resolutions provides complementary supervision across spatial scales. Trained on 577 publicly available HEST samples, MINT achieves the best overall performance on both HEST-Bench for gene expression prediction (mean Pearson r = 0.440) and EVA for general pathology tasks (0.803), demonstrating that spatial transcriptomics supervision complements morphology-centric self-supervised pretraining.

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Minsoo Lee, Jonghyun Kim, Juseung Yun, Sunwoo Yu, Jongseong Jang. 2026-03-09. MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models. https://arxiv.org/abs/2603.07895

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