Query: The proposed framework for multimodal learning in healthcare aims to integrate diverse medi-
cal data, including images, text, and protein sequences, for improved diagnosis and treatment
[Ding et al., 2024]. The challenge lies in combining spatial data from images, contextual infor-
mation from text, and molecular insights from protein sequences in a scalable and robust manner
[Li et al., 2024]. Existing fusion techniques are limited by their inability to handle large, heteroge-
neous datasets effectively [Quevedo et al., 2023, Basystiuk and Melnykova, 2023, Mohsen et al., 2022].
Recent advances in multimodal learning have shown promising results in medical image classi-
fication [Ding et al., 2024], disease classification [Yao et al., 2024], and clinical description genera-
tion [Yao et al., 2024]. For instance, Cross-Graph Modal Contrastive Learning (CGMCL) has been
proposed for multimodal medical image classification, leveraging contrastive learning to align mul-
timodal features in a shared latent space (Ding et al. 20241). CGMCL has been evaluated on
two datasets, a Parkinson’s disease (PD) dataset and a public melanoma dataset, demonstrating
improved accuracy and interpretability compared to conventional unimodal methods (Ding et al.
20241).
Other approaches, such as graph attention networks (GATs), have also been proposed for mul-
timodal fusion, enabling the learning and fusion of neighboring node features [Quevedo et al., 2023,
Ding et al., 2024]. Attention mechanisms have been applied to tabular data to improve model per-
formance and interpretability, while multimodal transformers have been used to combine image and
clinical data for predicting AD progression [Quevedo et al., 2023, Ding et al., 2024].
In the context of multimodal medical image classification, the proposed framework leverages
low-rank approximations and graph neural networks (GNNs) for enhanced cross-modal integration
[Ding et al., 2024]. The framework processes three primary modalities: images, text, and protein
sequences [Ding et al., 2024]. For images, it uses NIH-CXR (X-ray images) and HPA (microscopy
images), applying normalization and resizing during preprocessing [Ding et al., 2024]. For text,
MIMIC-CXR (clinical reports) is processed using Clinical BERT embeddings [Yao et al., 2024]. Pro-
tein sequences from UniProt are encoded with ESM-1b [Ding et al., 2024].
The methodology involves extracting features from each modality using state-of-the-art mod-
els, such as Vision Transformers (ViT) for images, Clinical BERT for text, and ESM-1b for pro-
tein sequences [Ding et al., 2024]. To manage the high-dimensional data, a low-rank approxima-
tion is applied, reducing the computational burden by approximating the concatenated feature
matrix [Ding et al., 2024]. The multimodal data is then modeled as a heterogeneous graph, with
nodes representing the different feature embeddings and edges capturing the relationships between
them [Ding et al., 2024]. A GNN is employed to refine these embeddings through message passing
[Ding et al., 2024].
The training of the model is done by minimizing a total loss function, which includes the task-
specific loss, an alignment term that brings image and text embeddings closer, and a regularization
term that enforces smoothness across the graph [Ding et al., 2024]. The proposed framework aims
to improve efficiency and scalability while integrating multimodal data for advanced healthcare
Subject: computer science
Intent: Other