\(\text {I}^2\) Net: Exploiting Misaligned Contexts Orthogonally with Implicit-Parameterized Implicit Functions for Medical Image Segmentation

Recent medical image segmentation methods have started to apply implicit neural representation (INR) to segmentation networks to learn continuous data representations. Though effective, they suffer from inferior performance. In this paper, we delve into the inferiority and discover that the underlying reason behind it is the indiscriminate treatment for context fusion that fails to properly exploit misaligned contexts. Therefore, we propose a novel Implicit-parameterized INR Network (I \(^{2}\) Net), which dynamically generates the model parameters of INRs to adapt to different misaligned contexts. We further propose novel gate shaping and learner orthogonalization to induce I \(^2\) Net to handle misaligned contexts in an orthogonal way. We conduct extensive experiments on two medical datasets, i.e. Glas and Synapse, and a generic dataset, i.e. Cityscapes, to show the superiority of our I \(^2\) Net. Code: https://github.com/ChineseYjh/I2Net .