IDA-NET: Individual Difference aware Medical Image Segmentation with Meta-Learning

Abstract

Individual differences in organ size and spatial distribution can lead to significant variations in the content of medical images at similar anatomical locations. These case-level differences are distinct from the domain shift between multi-source data, yet they can significantly affect model performance and are difficult to address using traditional transfer learning algorithms such as domain generalization. To address the individual difference issue, we propose an individual difference aware meta-learning strategy and introduce an individual discriminator module. These components are designed to learn features related to individual difference, enhancing the model’s ability to accurately segment organs across different patients. Additionally, we present a Transformer-based U-Net framework that captures both long- and short-range dependencies from MR images. This framework utilizes a parallel attention module to address the limitations of self-attention and employs an inter-layer attention module to extract attention relationships across different layers. We evaluate our approach using the Synapse dataset. Results indicate that focusing on individual difference not only significantly improves the performance of various sub-modules, allowing our method to surpass several state-of-the-art methods, but also proves to be beneficial for many other methods as well.