Improving medical image analysis using advanced deep learning techniques
High-performance deep neural networks for medical image analysis
This study is working on making AI tools for medical imaging better and more reliable, so patients can get more accurate diagnoses and treatments.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11288210 on NIH RePORTER |
What this research studies
This research focuses on enhancing the reliability and performance of deep neural networks (DNNs) used in medical image analysis. By developing a novel contrastive feature analysis (CFA) framework, the project aims to visualize complex data and refine DNN architectures for better accuracy in diagnosing and treating diseases. Patients can benefit from improved AI-driven diagnostic tools that are more transparent and trustworthy. The research will involve analyzing high-dimensional feature data to ensure that the DNNs are effectively trained and tested.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients undergoing medical imaging procedures who may benefit from enhanced diagnostic accuracy.
Not a fit: Patients who do not require medical imaging or those with conditions not addressed by the AI tools developed in this research may not receive any benefit.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and reliable AI tools for diagnosing and treating various medical conditions.
How similar studies have performed: Other research has shown promising results in improving AI applications in medical imaging, indicating that this approach has the potential for significant advancements.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Islam, Md Tauhidul — Stanford University
- Study coordinator: Islam, Md Tauhidul
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.