🔬 Multimodal Framework for Efficient Cancer Survival Prediction
Cancer survival prediction is one of the most critical challenges in healthcare 🏥. Accurate estimation not only supports better medical decision-making but also empowers doctors to deliver personalized treatment plans 🎯. By identifying high-risk patients early, healthcare providers can ensure timely interventions, leading to improved treatment outcomes 💡.
🌐 The Challenge
Traditional methods often rely on single-modality data (either images or genetic information) ❗. While useful, these approaches miss the bigger picture, and many models suffer from excessive computational complexity ⏳—limiting their real-world use in large-scale medical datasets.
💡 Our Solution
To overcome these hurdles, we propose a novel multimodal survival prediction framework that combines Whole Slide Images (WSI) 🖼️ with genomic data 🧬.
✨ Key Innovations:
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Attention Mechanisms 🔎 – Capture complex correlations within and across both modalities.
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Locality-Sensitive Hashing ⚡ – Optimizes self-attention, significantly cutting down computational costs.
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Scalability 📈 – Efficiently processes large, high-resolution datasets for practical clinical use.
📊 Results
Experiments on the TCGA-BLCA dataset confirm that integrating WSI and genomic data outperforms unimodal methods 🚀. The optimized attention mechanism ensures high predictive accuracy while being resource-efficient—making it highly suitable for large-scale applications.
✅ Conclusion
Our framework offers a robust, scalable, and efficient solution for cancer survival prediction. By leveraging multimodal integration and optimized attention, this approach paves the way for AI-powered clinical decision support systems 🧑⚕️🤖, transforming the future of precision medicine.
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