The field of medical image analysis is witnessing significant developments, with a focus on improving the accuracy and efficiency of image segmentation, feature extraction, and disease diagnosis. Researchers are exploring novel deep learning-based approaches, such as dual-branch feature extraction frameworks and semi-supervised learning methods, to enhance the integration of complementary information from multi-modal images. These innovations have the potential to facilitate early disease detection, personalized treatment, and improved patient outcomes. Noteworthy papers include:

• RPFNet, which proposes a residual prior-driven frequency-aware network for image fusion, achieving efficient global feature modeling and integration.
• DSANet, which introduces a dual semantic-aware network for noise-suppressed ultrasound video segmentation, demonstrating substantial improvements in segmentation accuracy and noise robustness.
• ArteryX, which presents a semi-supervised artery evaluation framework for advancing brain artery feature extraction, offering a robust validation framework and achieving high accuracy and efficiency.