5 accepted papers
The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods often rely on resource-heavy Large Language Models (LLMs) or static knowledge graphs and struggle with two fundamental chall
Retinal image analysis is crucial for diagnosing and treating eye diseases, yet generating accurate medical reports from images remains challenging due to variability in image quality and pathology, especially with limited labeled data. Previous Transformer-based models struggled to integrate visual…
Deep learning’s requirements for large amounts of training data remains a challenge for researchers and developers. Generative Adversarial Network (GAN) is commonly used in medical image analysis to generate novel training images to help resolve this issue. While deep learning has many clinical appl…
Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant dose reduction during CT scans while maintaining the diagnostic image quality. MBIR improves the image quality over analytical reconstruction by modeling both the sensor (e.g., forwa…
Despite the recent flight control regulations, Unmanned Aerial Vehicles (UAVs) are still gaining popularity in civilian and military applications, as much as for personal use. Such emerging interest is pushing the development of effective collision avoidance systems. Such systems play a critical rol…