MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

• URL: http://arxiv.org/abs/2503.00374v1
• Date: Sat, 01 Mar 2025 07:02:30 GMT
• Title: MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
• Authors: Tianyi Wang, Jianan Fan, Dingxin Zhang, Dongnan Liu, Yong Xia, Heng Huang, Weidong Cai,
• Abstract summary: Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
• Score: 52.106879463828044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific structures. However, unlike conventional scenarios where multi-modal inputs share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics delineates molecular signatures through gene expression patterns. This inherent disparity introduces a major challenge in aligning them while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive features for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and benefiting the cancer diagnosis.

Related papers

• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification. We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images [1.3124513975412255]
  spatial transcriptomics (ST) enables transcriptome-wide gene expression profiling while preserving spatial context. Current spatial clustering methods fail to fully integrate high-resolution histology image features with gene expression data. We propose a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features.
  arXiv  Detail & Related papers  (2024-10-31T00:32:24Z)
• Multi-Cohort Framework with Cohort-Aware Attention and Adversarial Mutual-Information Minimization for Whole Slide Image Classification [3.1406146587437904]
  We propose a novel approach for multi-cohort WSI analysis, designed to leverage the diversity of different tumor types. We introduce a Cohort-Aware Attention module, enabling the capture of both shared and tumor-specific pathological patterns. We also develop a hierarchical sample balancing strategy to mitigate cohort imbalances and promote unbiased learning.
  arXiv  Detail & Related papers  (2024-09-17T12:18:00Z)
• Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading [15.492493684721175]
  multimodal deep learning has achieved a promising performance in glioma grading. We introduce a novel Focus on Focus (FoF) framework with paired pathology-genomic training and applicable pathology-only inference. Experiments on the TCGA GBM-LGG dataset demonstrate that our FoF framework significantly improves the glioma grading.
  arXiv  Detail & Related papers  (2024-08-16T04:54:10Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation [68.63955715643974]
  Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o) We propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o)
  arXiv  Detail & Related papers  (2024-07-08T01:06:13Z)
• Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology [8.802214988309684]
  We introduce a hierarchical attention structure to leverage shared and complementary features of both modalities of histology and genomics. Our method surpasses previous state-of-the-art approaches in glioma diagnosis and prognosis tasks.
  arXiv  Detail & Related papers  (2024-06-11T09:06:41Z)
• Learning Invariant Molecular Representation in Latent Discrete Space [52.13724532622099]
  We propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts.
  arXiv  Detail & Related papers  (2023-10-22T04:06:44Z)
• A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion [54.512440195060584]
  We propose the Unified Multi-Modal Conditional Score-based Generative Model (UMM-CSGM) to take advantage of Score-based Generative Model (SGM) UMM-CSGM employs a novel multi-in multi-out Conditional Score Network (mm-CSN) to learn a comprehensive set of cross-modal conditional distributions. Experiments on BraTS19 dataset show that the UMM-CSGM can more reliably synthesize the heterogeneous enhancement and irregular area in tumor-induced lesions.
  arXiv  Detail & Related papers  (2022-07-07T16:57:21Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.