FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring
This addresses the need for interpretable image fusion in brain tumor monitoring, which is crucial for clinical decision-making, though it appears incremental as it builds on existing fusion methods with a focus on decomposability.
The authors tackled the problem of non-interpretable fused images in medical diagnostics by introducing FusionINN, a decomposable image fusion framework that can generate and decompose fused images, achieving faster and qualitatively better results than a recent diffusion-based model.
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pathology. We introduce FusionINN, a novel decomposable image fusion framework, capable of efficiently generating fused images and also decomposing them back to the source images. FusionINN is designed to be bijective by including a latent image alongside the fused image, while ensuring minimal transfer of information from the source images to the latent representation. To the best of our knowledge, we are the first to investigate the decomposability of fused images, which is particularly crucial for life-sensitive applications such as medical image fusion compared to other tasks like multi-focus or multi-exposure image fusion. Our extensive experimentation validates FusionINN over existing discriminative and generative fusion methods, both subjectively and objectively. Moreover, compared to a recent denoising diffusion-based fusion model, our approach offers faster and qualitatively better fusion results.