Deep Learning Approaches for Occlusion Removal in Medical Images
Kaluva Jaya Deepthi, Bangole Narendra Kumar Rao · 2024
Medical imaging is essential for accurate diagnosis and effective treatment planning. However, medical images often suffer from occlusions caused by various factors such as artifacts, noise, or anatomical structures. These occlusions can impede accurate analysis and interpretation, leading to potential misdiagnosis or treatment errors. Addressing occlusion removal in medical images is thus of paramount importance. This work examines the state-of-the-art deep learning techniques for occlusion removal in medical imaging, focusing on convolutional neural networks, autoencoders, and generative adversarial networks (GANs). These models utilize robust feature extraction and reconstruction capabilities to restore occluded regions with high accuracy, preserving critical anatomical structures and pathological details.