CanLRHI: a multimodal pretraining model for cell death analysis in cancer pathology based on long-text representation and high-resolution images.

Abstract

The high heterogeneity of cancer poses significant challenges for precision diagnosis, particularly in tasks such as rare subtype identification, early lesion detection, and tumor grading. Notably, cancer cell biological traits are closely correlated with cell death regulatory mechanisms, and accurate cancer region identification is a pivotal premise for exploring the cancer-cell death intrinsic association. Single-modality methods often struggle to balance sensitivity and accuracy, while existing general multimodal models are poorly adapted to the processing of pathological long texts and high-resolution images, leading to issues of semantic truncation and feature loss. To address these challenges, this study proposes CanLRHI, a multimodal pretraining model tailored for cancer pathology that focuses on the synergistic modeling of long pathological reports and high-resolution images to achieve comprehensive cross-modal alignment and accurate characterization of cancer regions. Experimental results on the CancerPath-170 K-v1 dataset, which contains 170 000 cancer pathology image-text pairs, demonstrate that CanLRHI significantly outperforms mainstream multimodal baselines across various tasks, including Zero-Shot classification and Few-Shot Fine-Tuning. This work provides an extensible technical framework for long-text-driven cross-modal representation learning in medical pathology, and further offers a reliable technical support for cell death-related cancer pathology research via high-precision cancer region detection.