Here, I showcase my journey as a deep learning scientist working on cutting-edge AI projects. You'll find a glimpse into my research experiences and the impact of my work in the field of medical imaging. Join me in the quest for improved cancer diagnosis and treatment through the power of AI.
Artificial intelligence (AI) is revolutionizing medical imaging by enabling accurate diagnosis, segmentation, and treatment planning—especially in complex, multi-modality cases like pelvic cancers. Unlike natural image domains, medical data is limited, heterogeneous, and clinically sensitive. This demands models that are not only accurate, but also robust, interpretable, and aligned with clinical standards. Explainability is key to bridging AI research with real-world deployment in healthcare settings.
My research leverages architectures such as GANs, diffusion models, transformers, and attention-based neural networks to tackle complex challenges in tumor segmentation and radiotherapy planning. By integrating cutting-edge technologies with rigorous scientific methodologies, I strive to develop innovative solutions that have a meaningful clinical impact. I am deeply committed to continuous learning and interdisciplinary collaboration, always seeking to bridge the gap between advanced AI techniques and real-world medical applications.
MED-INPAINT: Medical Image Synthesis using Multi-Level Conditional Inpainting with a Denoising Diffusion Probalistic Model and Adaptive Contrast Priors
Spatial-Adaptive Deep Learning Model and Magnetic Resonance Fingerprinting for Segmentation and Quantitative Evaluation of Cervical Cancer
Organs-at-Risk Segmentation on T2-Weighted Magnetic Resonance Imaging using Transformer-Based Segmentation
Attended MICCAI 2022, the biggest medical image processing conference, in Singapore
Contrast-Enhanced to Non-Contrast CT Synthesis using Cycle-Consistent Generative Adversarial Network (Cycle-GAN) for Radiomics and Deep Learning in the Era of COVID-19
The MIDL conference is held annually and encompasses the most recent deep learning-based research and innovative approaches across a wide range of medical imaging applications
Showcase of recent cancer research studies at the Institute of Cancer Research, London
