Welcome to AID Lab

Medical image segmentation involves the process of identifying and delineating specific structures or regions of interest within a medical image. The goal of medical image segmentation is to partition an image into distinct and meaningful segments. This segmentation process plays a crucial role in medical diagnosis, treatment planning, and research, as it enables healthcare professionals and researchers to analyze and understand the spatial distribution, size, shape, and characteristics of specific regions within the image.

Classification of medical images is a crucial task in the field of medical imaging and computer vision. It involves categorizing or labeling different regions or objects within a medical image into predefined classes or categories. The goal is to assist healthcare professionals and researchers in making accurate diagnoses, treatment decisions, and predictions based on the information present in the images.

Topology-aware segmentation in the context of medical imaging refers to a segmentation process that takes into account the spatial relationships and connectivity between different regions or structures within an image. This approach considers not only the visual characteristics of individual regions but also their interactions and arrangements in a higher-dimensional space.

Classification of medical images using attention mechanisms, particularly in the context of deep learning, involves enhancing the model's ability to focus on relevant regions or features within the image while making classification decisions. Attention mechanisms allow the model to dynamically allocate more importance to certain parts of the image, thereby improving the model's interpretability and performance.