Learnable weight initialization for volumetric medical image segmentation

Shahina K. Kunhimon, Abdelrahman M. Shaker, Muzammal Naseer, Salman H. Khan, Fahad Shahbaz Khan

Abstract: Highlights•We propose a learnable weight initialization method that can be integrated into any hybrid volumetric medical segmentation model to effectively train small-scale datasets.•To learn such a weight initialization, we propose data-dependent self-supervised objectives tailored to learn the structural and contextual cues from the volumetric medical image datasets.•We demonstrate the effectiveness of our approach by conducting experiments for multi-organ and tumor segmentation tasks, achieving superior segmentation performance without requiring additional external training data.