Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology

Developing a central nervous system (CNS) tumor classifier by integrating DNA methylation data with Whole Slide Images (WSI) offers significant potential for enhancing diagnostic precision in neuropathology. Existing approaches typically integrate encoded omic data with histology only once - either at an early or late fusion stage - while reintroducing encoded omic data to create a dual fusion variant remains unexplored. Nevertheless, reintroduction of omic embeddings during early and late fusion enables the capture of complementary information from localized patch-level and holistic slide-level interactions, allowing boosted performance through advanced multimodal integration. To achieve this, we propose a dual fusion framework that integrates omic data at both early and late stages, fully leveraging its diagnostic strength. In the early fusion stage, omic embeddings are projected into a patch-wise latent space, generating omic-WSI embeddings that encapsulate per-patch molecular and morphological insights, effectively incorporating this information into the spatial representation of histology. These embeddings are refined with a multiple instance learning gated attention mechanism to attend to critical patches. In the late fusion stage, we reintroduce the omic data by fusing it with slide-level omic-WSI embeddings using a Multimodal Outer Arithmetic Block (MOAB), which richly intermingles features from both modalities, capturing their global correlations and complementarity. We demonstrate accurate CNS tumor subtyping across 20 fine-grained subtypes and validate our approach on benchmark datasets, achieving improved survival prediction on TCGA-BLCA and competitive performance on TCGA-BRCA compared to state-of-the-art methods. This dual fusion strategy enhances interpretability and classification performance, highlighting its potential for clinical diagnostics.

MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading

Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patients prognosis, the higher the grade, the more aggressive the tumor. Correct diagnosis of a tumor grade remains challenging. Though histopathological grading has been shown to be prognostic, results are subject to interobserver variability, even among experienced pathologists. Recently, the World Health Organization reported that advances in molecular genetics have led to improvements in tumor classification. This paper seeks to integrate histological images and genetic data for improved computer-aided diagnosis. We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade \rom{2}, \rom{3} and \rom{4}). Extensive experiments evaluate the effectiveness of our approach. By applying MOAB to The Cancer Genome Atlas (TCGA) glioma dataset, we show that it can improve separation between similar classes (Grade \rom{2} and \rom{3}) and outperform prior state-of-the-art grade classification techniques.

FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classification

Fusion of multimodal healthcare data holds great promise to provide a holistic view of a patient's health, taking advantage of the complementarity of different modalities while leveraging their correlation. This paper proposes a simple and effective approach, inspired by attention, to fuse discriminative features from different modalities. We propose a novel attention mechanism, called Flattened Outer Arithmetic Attention (FOAA), which relies on outer arithmetic operators (addition, subtraction, product, and division) to compute attention scores from keys, queries and values derived from flattened embeddings of each modality. We demonstrate how FOAA can be implemented for self-attention and cross-attention, providing a reusable component in neural network architectures. We evaluate FOAA on two datasets for multimodal tumor classification and achieve state-of-the-art results, and we demonstrate that features enriched by FOAA are superior to those derived from other fusion approaches. The code is publicly available at \href{https://github.com/omniaalwazzan/FOAA}{https://github.com/omniaalwazzan/FOAA}