[Paper Review] Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis

Ziyan Yao, Fei Lin+4 more|arXiv (Cornell University)|May 23, 2024

Radiomics and Machine Learning in Medical ImagingMedicine3 citations

TL;DR

This paper proposes a multimodal deep learning framework that integrates medical images and clinical reports using convolutional neural networks for image feature extraction and a bidirectional LSTM with attention for text understanding. The model achieves superior performance in disease classification, lesion localization, and clinical report generation by effectively fusing visual and textual representations through a dedicated multimodal fusion layer.

ABSTRACT

In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to realize the joint representation learning of image and text. In the empirical study, we selected a large medical image database covering a variety of diseases, combined with corresponding clinical reports for model training and validation. The proposed multimodal deep learning model demonstrated substantial superiority in the realms of disease classification, lesion localization, and clinical description generation, as evidenced by the experimental results.

Motivation & Objective

• To address the challenge of integrating heterogeneous medical data—specifically medical images and clinical reports—into a unified representation for advanced disease analysis.
• To improve the accuracy and interpretability of disease classification by leveraging both visual and textual clinical information.
• To enable precise lesion localization by fusing spatial features from medical images with clinically relevant text statements.
• To generate comprehensive and accurate clinical descriptions by jointly modeling image and report modalities.
• To develop a robust multimodal fusion mechanism that captures cross-modal dependencies for enhanced diagnostic reasoning.

Proposed method

• Convolutional neural networks (CNNs) are used to extract high-dimensional, spatially aware features from medical images, capturing focal details, texture, and spatial distribution.
• A bidirectional long short-term memory (Bi-LSTM) network with an attention mechanism processes clinical reports to capture deep semantic representations and highlight disease-relevant statements.
• A custom multimodal fusion layer enables effective interaction and integration between image and text features, enabling joint representation learning.
• The fusion mechanism dynamically attends to relevant image and text features, enhancing cross-modal alignment and contextual understanding.
• The model is trained end-to-end on a large-scale medical image database paired with corresponding clinical reports, using supervised learning objectives for classification, localization, and generation tasks.
• The architecture supports multi-task learning, allowing simultaneous optimization for disease classification, lesion localization, and clinical report generation.

Experimental results

Research questions

• RQ1 How effectively can a multimodal deep learning model integrate visual features from medical images with semantic features from clinical reports for improved diagnostic performance?
• RQ2 To what extent does the proposed attention-enhanced Bi-LSTM improve the identification of clinically relevant statements in radiology reports?
• RQ3 Can the multimodal fusion layer significantly enhance joint representation learning compared to modality-specific or early fusion baselines?
• RQ4 How does the model perform in lesion localization tasks when combining image-level features with textual descriptions?
• RQ5 Can the model generate clinically coherent and accurate descriptions by leveraging both image and report modalities?

Key findings

• The proposed model demonstrated substantial superiority in disease classification, outperforming unimodal and early fusion baselines on the benchmark dataset.
• Lesion localization performance was significantly improved due to the effective alignment of image regions with relevant textual descriptions through the fusion mechanism.
• The model achieved state-of-the-art results in clinical report generation, producing descriptions that were both semantically accurate and clinically relevant.
• The attention mechanism in the Bi-LSTM effectively highlighted key diagnostic phrases in clinical reports, improving interpretability and model focus.
• The multimodal fusion layer enabled robust cross-modal feature interaction, leading to more reliable and generalizable predictions across diverse disease types.
• Empirical evaluation confirmed that joint learning of classification, localization, and generation tasks led to improved performance across all three downstream tasks.