Multi-Modal Deep Learning for Medical Image Analysis

Medical diagnosis typically involves integrating information from multiple sources, including medical images, clinical notes, laboratory results, and patient history. However, most machine learning approaches for medical diagnosis focus on single modalities, potentially missing important complementary information.In this work, we develop a multi-modal deep learning framework that combines medical imaging with clinical text data to improve diagnostic accuracy. Our approach uses vision transformers for image analysis and BERT-based models for text processing, with a novel cross-modal attention mechanism that enables effective information fusion.We evaluate our approach on three medical conditions: lung cancer detection, cardiovascular disease diagnosis, and neurological disorder classification. Our multi-modal approach achieves significant improvements over single-modal baselines, with an average 12% improvement in early disease detection accuracy.

Our multi-modal framework consists of three main components: (1) Vision Transformer for medical image analysis that processes radiological images and extracts visual features, (2) Clinical BERT for text processing that analyzes clinical notes and extracts semantic features, and (3) Cross-Modal Fusion Network that combines visual and textual information using attention mechanisms.

The vision transformer is pre-trained on a large corpus of medical images and fine-tuned for specific diagnostic tasks. The clinical BERT model is trained on clinical text data and captures medical terminology and relationships. The cross-modal fusion network uses multi-head attention to identify relevant correspondences between visual and textual features.

We conducted experiments on three large-scale medical datasets: (1) Lung cancer detection using chest CT scans and radiology reports (50,000 cases), (2) Cardiovascular disease diagnosis using echocardiograms and clinical notes (30,000 cases), and (3) Neurological disorder classification using brain MRI and clinical assessments (25,000 cases).

Our multi-modal approach achieved: 94.3% accuracy for lung cancer detection (vs. 84.1% image-only), 91.7% for cardiovascular disease (vs. 82.4% image-only), and 88.9% for neurological disorders (vs. 79.2% image-only). The improvements were particularly pronounced for early-stage diseases where clinical context is crucial for accurate diagnosis.