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.

Going Beyond H&E and Oncology: How Do Histopathology Foundation Models Perform for Multi-stain IHC and Immunology?

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench.

Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images

Whole Slide Images (WSIs) present a challenging computer vision task due to their gigapixel size and presence of numerous artefacts. Yet they are a valuable resource for patient diagnosis and stratification, often representing the gold standard for diagnostic tasks. Real-world clinical datasets tend to come as sets of heterogeneous WSIs with labels present at the patient-level, with poor to no annotations. Weakly supervised attention-based multiple instance learning approaches have been developed in recent years to address these challenges, but can fail to resolve both long and short-range dependencies. Here we propose an end-to-end multi-stain self-attention graph (MUSTANG) multiple instance learning pipeline, which is designed to solve a weakly-supervised gigapixel multi-image classification task, where the label is assigned at the patient-level, but no slide-level labels or region annotations are available. The pipeline uses a self-attention based approach by restricting the operations to a highly sparse k-Nearest Neighbour Graph of embedded WSI patches based on the Euclidean distance. We show this approach achieves a state-of-the-art F1-score/AUC of 0.89/0.92, outperforming the widely used CLAM model. Our approach is highly modular and can easily be modified to suit different clinical datasets, as it only requires a patient-level label without annotations and accepts WSI sets of different sizes, as the graphs can be of varying sizes and structures. The source code can be found at https://github.com/AmayaGS/MUSTANG.

Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue

Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue. It is a highly heterogeneous disease, with wide cellular and molecular variability observed in synovial tissues. Over the last two decades, the methods available for their study have advanced considerably. In particular, Immunohistochemistry stains are well suited to highlighting the functional organisation of samples. Yet, analysis of IHC-stained synovial tissue samples is still overwhelmingly done manually and semi-quantitatively by expert pathologists. This is because in addition to the fragmented nature of IHC stained synovial tissue, there exist wide variations in intensity and colour, strong clinical centre batch effect, as well as the presence of many undesirable artefacts present in gigapixel Whole Slide Images (WSIs), such as water droplets, pen annotation, folded tissue, blurriness, etc. There is therefore a strong need for a robust, repeatable automated tissue segmentation algorithm which can cope with this variability and provide support to imaging pipelines. We train a UNET on a hand-curated, heterogeneous real-world multi-centre clinical dataset R4RA, which contains multiple types of IHC staining. The model obtains a DICE score of 0.865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres. It can be used as the first step in an automated image analysis pipeline for synovial tissue samples stained with IHC, increasing speed, reproducibility and robustness.