A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
This work addresses a problem for medical professionals by improving image fusion to aid in clinical diagnosis and surgical procedures, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the loss of diagnostic edge details and contrast in multimodal medical image fusion by proposing a model based on integrated Laplacian-Gaussian concatenation with attention pooling, which effectively preserves complementary information and important tissue structures.
Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients' disorders and provide guidance during preoperative and intra-operative procedures. Deep learning (DL) models have achieved end-to-end image fusion with highly robust and accurate fusion performance. However, most DL-based fusion models perform down-sampling on the input images to minimize the number of learnable parameters and computations. During this process, salient features of the source images become irretrievable leading to the loss of crucial diagnostic edge details and contrast of various brain tissues. In this paper, we propose a new multimodal medical image fusion model is proposed that is based on integrated Laplacian-Gaussian concatenation with attention pooling (LGCA). We prove that our model preserves effectively complementary information and important tissue structures.