Learning to Transform: Unifying Latent Geometric Shape and Appearance Representations in Healthcare Imaging

Recent advances in deep neural networks have highlighted the importance of geometric shape in various image analysis and computer vision tasks. However, most current approaches rely on coarse or simplified shape representations, such as binary masks, meshes, or point clouds, that are primarily designed to capture global structures of objects presented in images. While effective for general image and visual understanding, these methods often fail to learn fine-grained geometric information that is critical for accurately modeling complex shapes and subtle anatomical variations. This limitation is particularly consequential in healthcare applications, where understanding fine-grained anatomical shapes and their changes is crucial for accurate disease detection and diagnosis. My research focuses on developing a set of advanced deep learning frameworks that learn robust and complex shape representations from dense image data and integrate them into the current paradigm of image appearance and texture learning.

Authors

• Tonmoy Hossain Graduate Research Assistant

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