The field of medical image analysis is rapidly evolving, with a focus on developing innovative architectures that combine the strengths of convolutional neural networks (CNNs) and transformers. Recent research has shown that integrating transformer-based architectures with CNNs can effectively capture global contextual information and long-range dependencies, leading to improved performance in image segmentation tasks. Notably, the use of attention mechanisms and multimodal learning has emerged as a promising direction, enabling more accurate and robust segmentation of medical images.

Particularly noteworthy papers include:

• Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation, which proposes a novel network that combines convolutional and transformer components to enhance boundary precision and robustness.
• ConMatFormer, a hybrid deep learning architecture that integrates ConvNeXt blocks, multiple attention mechanisms, and transformer modules to accurately classify diabetic foot ulcers.
• FT-ARM, a fine-tuned multimodal language model that achieves high accuracy in pressure ulcer severity classification and provides clinically grounded natural-language explanations.
• FlexICL, a flexible in-context learning framework that enables efficient segmentation of musculoskeletal structures in ultrasound images with limited labeled data.
• SA$^{2}$Net, a scale-adaptive structure-aware network that achieves superior spine segmentation performance from ultrasound volume projection imaging.