Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction

Background and Objective:

Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale information and misalignment of inter-scale features. Our study introduces the Integrated-Scale Pyramidal Interactive Reconfiguration to Enhance feature learning (INSPIRE).

Methods:

INSPIRE focuses on intra-scale semantic enhancement and precise inter-scale spatial alignment, integrated with a novel spatial-semantic back augmentation technique. We evaluated INSPIRE’s efficacy using standard hierarchical neural networks, such as UNet and FPN, across multiple medical segmentation challenges including brain tumors and polyps. Additionally, we extended our evaluation to object detection and semantic segmentation in natural images to assess generalizability.

Results:

INSPIRE demonstrated superior performance over standard baselines in medical segmentation tasks, showing significant improvements in feature learning and alignment. In identifying brain tumors and polyps, INSPIRE achieved enhanced precision, sensitivity, and specificity compared to traditional models. Further testing in natural images confirmed the adaptability and robustness of our approach.

Conclusions:

INSPIRE effectively enriches semantic clarity and aligns multi-scale features, achieving integrated spatial-semantic coherence. This method seamlessly integrates with existing frameworks used in medical image analysis, thereby promising to significantly enhance the efficacy of computer-aided diagnostics and clinical interventions. Its application could lead to more accurate and efficient imaging processes, essential for improved patient outcomes.