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) holds great potential in the field of medical imaging, as it enables the early and precise diagnosis of diseases, thereby enhancing patient outcomes. One of the most challenging aspects of medical imaging is the segmentation of tumors, particularly those located in the pelvic region, which are often difficult to detect and isolate.
As a researcher, I am dedicated to exploring the latest generative models such as GANs and diffusion models to facilitate domain adaptation, along with advanced 3D techniques, such as transformers and attention neural networks, to enhance the segmentation of tumors on magnetic resonance imaging (MRI). By combining cutting-edge technology and rigorous scientific methods, I am committed to pushing the boundaries of what's possible in the field of medical imaging, and ultimately improving patient care.
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
