arXiv:2503.00374v1 Announce Type: cross
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.
MULTI-MODAL SELF-SUPERVISED LEARNING IN ONCOLOGY
In the field of oncology, the combination of histopathology and transcriptomics provides valuable insights into the morphology and molecular aspects of cancer. However, integrating these diverse data sources poses a challenge due to their inherent differences in characteristics. Conventional multi-modal integration methods tend to focus on aligning the modalities, but often fail to retain modality-specific structures. This is especially crucial in the case of histopathology and transcriptomics, where their distinct features offer unique and complementary information.
MIRROR: ADDRESSING THE CHALLENGES
To overcome these challenges, the researchers propose a novel multi-modal representation learning method called MIRROR. MIRROR takes into account both modality alignment and retention, providing a comprehensive solution for learning pathological representations.
MIRROR utilizes dedicated encoders to extract comprehensive features for each modality. This allows for the preservation of the specific attributes of histopathology and transcriptomics. Furthermore, MIRROR employs a modality alignment module to seamlessly integrate phenotype patterns and molecular profiles, bridging the gap between the morphology and gene expression patterns.
To ensure the uniqueness of each modality, MIRROR incorporates a modality retention module. This module safeguards the modality-specific attributes, preventing the loss of crucial information. Additionally, a style clustering module is incorporated to mitigate redundancy and enhance disease-relevant information. By modeling and aligning consistent pathological signatures within a clustering space, MIRROR maximizes the utility of the multi-modal data.
APPLICATIONS IN CANCER DIAGNOSIS
The effectiveness of MIRROR is extensively evaluated on TCGA cohorts for cancer subtyping and survival analysis. The results demonstrate its superior performance in constructing comprehensive oncological feature representations. By effectively integrating histopathology and transcriptomics, MIRROR provides valuable insights for cancer diagnosis.
IMPACT ON MULTIMEDIA INFORMATION SYSTEMS
The development of MIRROR contributes to the wider field of multimedia information systems. By integrating multi-modal data, MIRROR enhances the analysis and understanding of complex diseases, such as cancer. Its approach of balancing modality alignment and retention can be applied to other domains where diverse data sources need to be integrated. Additionally, MIRROR highlights the importance of multi-disciplinary collaboration, as it requires expertise from both the fields of oncology and information systems.
RELEVANCE TO ANIMATIONS, ARTIFICIAL REALITY, AUGMENTED REALITY, AND VIRTUAL REALITIES
While the focus of this article is on the application of MIRROR in oncology, the concepts and techniques discussed have relevance in the fields of animations, artificial reality, augmented reality, and virtual realities.
Animations, artificial reality, augmented reality, and virtual realities often involve the integration of different data sources and modalities to create immersive and interactive experiences. Just like in the case of histopathology and transcriptomics, the challenge lies in aligning and retaining the distinct characteristics of each modality. MIRROR’s approach of dedicated encoders, modality alignment, retention modules, and style clustering can be adapted to these fields to improve the integration and representation of multi-modal data.
In conclusion, the development of MIRROR and its applications in oncology demonstrate the importance of multi-modal self-supervised learning and the need for a balanced approach to modality alignment and retention. The concepts and techniques discussed in this article have far-reaching implications in the wider field of multimedia information systems, as well as in animations, artificial reality, augmented reality, and virtual realities.