Modality Missing Brain Image

Missing modality brain imaging presents a significant challenge in medical image analysis, hindering accurate diagnosis and treatment planning. Current research focuses on developing methods to synthesize missing MRI modalities from available data using techniques like generative adversarial networks (GANs) and transformer networks, often incorporating unsupervised or self-supervised learning strategies to leverage unpaired data. These approaches aim to improve the accuracy of downstream tasks such as brain tumor segmentation by generating realistic representations of the missing data, thereby enhancing the reliability of clinical workflows. The development of robust and accurate modality synthesis methods holds substantial promise for improving healthcare by enabling more comprehensive and reliable analyses even with incomplete datasets.

Papers