2025-2026 Faculty Research Grants

Ackley Will (Computing). 

AI for Medical Imaging.

With the increasing demand for automated diagnostic tools, our project leverages deep learning techniques to analyze complex medical images and detect multiple pathologies simultaneously. For chest X-rays, we will utilize large public datasets such as NIH ChestX-ray14, CheXpert, and MIMIC-CXR to develop models capable of identifying a wide range of pulmonary conditions. In parallel, MRI data will be employed to build robust diagnostic models aimed at detecting and characterizing tumors in critical areas, including the brain and other organs. The research will employ state-of-the-art convolutional neural networks and transformer-based architectures to extract meaningful features from these diverse imaging sources. Methods including transfer learning and data augmentation will be explored to enhance model performance and generalizability. The study will also investigate strategies to address challenges such as class imbalance and noisy annotations, ensuring the development of reliable and scalable diagnostic systems. By bridging the gap between advanced AI techniques and practical medical applications, this research aspires to contribute to more reliable and transparent diagnostic systems, ultimately setting a foundation for future advancements in AI-driven medical imaging and clinical decision support systems.