Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction

• URL: http://arxiv.org/abs/2306.08330v2
• Date: Mon, 11 Sep 2023 08:34:20 GMT
• Title: Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction
• Authors: Yingxue Xu and Hao Chen
• Abstract summary: Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death. Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis.
• Score: 5.445390550440809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs). 2) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis. Although current approaches attempt to model these interactions via co-attention between histology and genomic data, they focus on only dense local similarity across modalities, which fails to capture global consistency between potential structures, i.e. TME-related interactions of histology and co-expression of genomic data. To address these challenges, we propose a Multimodal Optimal Transport-based Co-Attention Transformer framework with global structure consistency, in which optimal transport (OT) is applied to match patches of a WSI and genes embeddings for selecting informative patches to represent the gigapixel WSI. More importantly, OT-based co-attention provides a global awareness to effectively capture structural interactions within TME for survival prediction. To overcome high computational complexity of OT, we propose a robust and efficient implementation over micro-batch of WSI patches by approximating the original OT with unbalanced mini-batch OT. Extensive experiments show the superiority of our method on five benchmark datasets compared to the state-of-the-art methods. The code is released.

Related papers

• Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery [56.622854875204645]
  We present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth gene-gene interactions. A novel weighted diversified sampling algorithm computes the diversity score of each data sample in just two passes of the dataset.
  arXiv  Detail & Related papers  (2024-10-21T03:35:23Z)
• Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images [10.996711454572331]
  Survival prediction, utilizing pathological images and genomic profiles, is increasingly important in cancer analysis and prognosis. Existing multimodal methods often rely on alignment strategies to integrate complementary information. We propose a Multimodal Cross-Task Interaction (MCTI) framework to explore the intrinsic correlations between subtype classification and survival analysis tasks.
  arXiv  Detail & Related papers  (2024-06-25T02:18:35Z)
• Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning [78.38442423223832]
  We develop a novel codebook pre-training task, namely masked microenvironment modeling. We demonstrate superior performance and training efficiency over state-of-the-art pre-training-based methods in mutation effect prediction.
  arXiv  Detail & Related papers  (2024-05-16T03:53:21Z)
• Must: Maximizing Latent Capacity of Spatial Transcriptomics Data [41.70354088000952]
  This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. It integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers.
  arXiv  Detail & Related papers  (2024-01-15T09:07:28Z)
• MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features [2.3942863352287787]
  Mutual-Guided Cross-Modality Transformer (MGCT) is a weakly-supervised, attention-based multimodal learning framework. We propose MGCT to combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment.
  arXiv  Detail & Related papers  (2023-11-20T10:49:32Z)
• Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction [43.1748594898772]
  We propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images. We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies.
  arXiv  Detail & Related papers  (2023-07-22T00:59:26Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• Low-rank Optimal Transport: Approximation, Statistics and Debiasing [51.50788603386766]
  Low-rank optimal transport (LOT) approach advocated in citescetbon 2021lowrank LOT is seen as a legitimate contender to entropic regularization when compared on properties of interest. We target each of these areas in this paper in order to cement the impact of low-rank approaches in computational OT.
  arXiv  Detail & Related papers  (2022-05-24T20:51:37Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

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.