Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation

Understand

Automatic tumor segmentation is a crucial step in medical image analysis for computer-aided diagnosis.

• Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation.
• This is because regular CNNs can only exploit translation invariance, ignoring further inherent symmetries existing in medical images such as rotations and reflections.
• To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.