Improvement and Application of Fusion Scheme in Automatic Medical Image Analysis

Abstract

The research in this paper provides generalization and new ideas for research topics in computer-assisted medicine. The main improvement efforts in deep learning-based multimodal fusion schemes, which provide alternative directions and robust feature fitting performance for fusion schemes, are building complex structures, migrating knowledge or experience, processing and enhancing data, and targeting features for semantic correction based on contextual features. At the application level, the brain, liver, and lungs are the main targets of scientific research, so this paper surveys related work and analyzes the reasons for performance gains. Taken together, deep learning-based image fusion schemes can assist physicians in understanding information about lesion sites, lesion types, and sizes, providing an important basis for developing personalized treatment plans, which is important for improving diagnosis and specifying precise treatment plans. Therefore, the investigation of medical image fusion schemes is promising and beneficial.

Multi-Modal Representation via Contrastive Learning with Attention Bottleneck Fusion and Attentive Statistics Features

“…One common strategy is feature-level fusion, where features from different sensors are extracted and combined to form a comprehensive representation [13,14]. This approach often utilizes traditional feature extraction algorithms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract useful features from image, audio, and textual data.…”