Scholarlink

<p>Kidney lesion subtype identification is essential for precise diagnosis and personalized treatment planning. However, achieving reliable classification remains challenging due to factors such as inter-patient anatomical variability, incomplete multi-phase CT acquisitions, and ill-defined or overlapping lesion boundaries. In addition, genetic and ethnic morphological variations introduce inconsistent imaging patterns, reducing the generalizability of conventional deep learning models. To address these challenges, we introduce a unified framework called Phase-aware Cross-Scale U-MAMba and Switch Atrous Bifovea EfficientNet B7 (PCU-SABENet), which integrates multi-phase reconstruction, fine-grained lesion segmentation, and robust subtype classification. The PhaseGAN-3D synthesizes missing CT phases using binary mask-guided inter-phase priors, enabling complete four-phase reconstruction even under partial acquisition conditions. The PCU segmentation module combines Contextual Attention Blocks, Cross-Scale Skip Connections, and uncertainty-aware pseudo-labeling to delineate lesion boundaries with high anatomical fidelity. These enhancements help mitigate low contrast and intra-class ambiguity. For classification, SABENet employs Switch Atrous Convolution for multi-scale receptive field adaptation, Hierarchical Tree Pooling for structure-aware abstraction, and Bi-Fovea Self-Attention to emphasize fine lesion cues and global morphology. This configuration is particularly effective in addressing morphological diversity across patient populations. Experimental results show that the proposed model achieves state-of-the-art performance, with 99.3% classification accuracy, 94.8% Dice similarity, 89.3% IoU, 98.8% precision, 99.2% recall, a phase-consistency score of 0.94, and a subtype confidence deviation of 0.08. Moreover, the model generalizes well on external datasets (TCIA) with 98.6% accuracy and maintains efficient computational performance, requiring only 0.138 GFLOPs and 8.2 ms inference time. These outcomes confirm the model’s robustness in phase-incomplete settings and its adaptability to diverse patient cohorts. The PCU-SABENet framework sets a new standard in kidney lesion subtype analysis, combining segmentation precision with clinically actionable classification, thus offering a powerful tool for enhancing diagnostic accuracy and decision-making in real-world renal cancer management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A phase-aware Cross-Scale U-MAMba with uncertainty-aware segmentation and Switch Atrous Bifovea EfficientNetB7 classification of kidney lesion subtype

  • 期刊论文

  • 发表日期：

    2025年9月30日

查看全文
  • Shamija Sherryl RMR,
  • Sudhan MB,
  • Deeptha R,
  • Thamizharasi M,
  • Vidyasri P

摘要

Kidney lesion subtype identification is essential for precise diagnosis and personalized treatment planning. However, achieving reliable classification remains challenging due to factors such as inter-patient anatomical variability, incomplete multi-phase CT acquisitions, and ill-defined or overlapping lesion boundaries. In addition, genetic and ethnic morphological variations introduce inconsistent imaging patterns, reducing the generalizability of conventional deep learning models. To address these challenges, we introduce a unified framework called Phase-aware Cross-Scale U-MAMba and Switch Atrous Bifovea EfficientNet B7 (PCU-SABENet), which integrates multi-phase reconstruction, fine-grained lesion segmentation, and robust subtype classification. The PhaseGAN-3D synthesizes missing CT phases using binary mask-guided inter-phase priors, enabling complete four-phase reconstruction even under partial acquisition conditions. The PCU segmentation module combines Contextual Attention Blocks, Cross-Scale Skip Connections, and uncertainty-aware pseudo-labeling to delineate lesion boundaries with high anatomical fidelity. These enhancements help mitigate low contrast and intra-class ambiguity. For classification, SABENet employs Switch Atrous Convolution for multi-scale receptive field adaptation, Hierarchical Tree Pooling for structure-aware abstraction, and Bi-Fovea Self-Attention to emphasize fine lesion cues and global morphology. This configuration is particularly effective in addressing morphological diversity across patient populations. Experimental results show that the proposed model achieves state-of-the-art performance, with 99.3% classification accuracy, 94.8% Dice similarity, 89.3% IoU, 98.8% precision, 99.2% recall, a phase-consistency score of 0.94, and a subtype confidence deviation of 0.08. Moreover, the model generalizes well on external datasets (TCIA) with 98.6% accuracy and maintains efficient computational performance, requiring only 0.138 GFLOPs and 8.2 ms inference time. These outcomes confirm the model’s robustness in phase-incomplete settings and its adaptability to diverse patient cohorts. The PCU-SABENet framework sets a new standard in kidney lesion subtype analysis, combining segmentation precision with clinically actionable classification, thus offering a powerful tool for enhancing diagnostic accuracy and decision-making in real-world renal cancer management.