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<p>The efficacy of automated lesion detection in clinical settings is often hampered by two primary factors: the vast range of pathological scales and the presence of non-target anatomical interference. Standard Mamba-based detectors, while efficient, frequently suffer from fixed receptive fields and background signal leakage. To resolve these challenges, we introduce ScaleMamba-YOLO, an enhanced medical object detection framework that integrates selective state-space modeling with adaptive local feature refinement. Our approach features two innovative structural components: first, a Medical Multi-scale Local Feature Enhancement Block (MMLFE-Block) is positioned at the frontend to diversify the initial receptive field. By utilizing a parallel architecture with heterogeneous convolutional kernels (1<InlineEquation ID="IEq100"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation>1, 3<InlineEquation ID="IEq200"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation>3, and 5<InlineEquation ID="IEq300"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation>5), the model achieves comprehensive hierarchical perception, enabling the concurrent identification of minute calcified spots and extensive diffuse lesions. Second, a Partial-Enhanced C2F (PEC2F) module is designed to refine feature aggregation post-global modeling. This component employs partial convolution (PConv) to selectively process salient feature channels, effectively filtering out irrelevant background noise from normal tissue structures. The robust performance of ScaleMamba-YOLO was validated across three specialized medical datasets (Br35H, BCCD, and PLoPy) and a standard scene dataset (VOC0712). The model recorded Average Precision (AP) scores of 72.7%, 65.0%, 85.7%, and 64.6%, respectively. These metrics represent consistent improvements of 1.7% to 2.3% over the MambaYOLO baseline, underscoring the system’s potential for high-fidelity diagnostic assistance in real-time clinical workflows.</p>

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ScaleMamba-YOLO: a multi-scale MambaYOLO for medical object detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Xiao Qin,
  • Quanmei Qian,
  • Xiaosen Li,
  • Chao Deng,
  • Wenji Wang,
  • Lei Peng,
  • Hongfei Lu,
  • Yuanxu Gong,
  • Jianbo Zhao

摘要

The efficacy of automated lesion detection in clinical settings is often hampered by two primary factors: the vast range of pathological scales and the presence of non-target anatomical interference. Standard Mamba-based detectors, while efficient, frequently suffer from fixed receptive fields and background signal leakage. To resolve these challenges, we introduce ScaleMamba-YOLO, an enhanced medical object detection framework that integrates selective state-space modeling with adaptive local feature refinement. Our approach features two innovative structural components: first, a Medical Multi-scale Local Feature Enhancement Block (MMLFE-Block) is positioned at the frontend to diversify the initial receptive field. By utilizing a parallel architecture with heterogeneous convolutional kernels (1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5), the model achieves comprehensive hierarchical perception, enabling the concurrent identification of minute calcified spots and extensive diffuse lesions. Second, a Partial-Enhanced C2F (PEC2F) module is designed to refine feature aggregation post-global modeling. This component employs partial convolution (PConv) to selectively process salient feature channels, effectively filtering out irrelevant background noise from normal tissue structures. The robust performance of ScaleMamba-YOLO was validated across three specialized medical datasets (Br35H, BCCD, and PLoPy) and a standard scene dataset (VOC0712). The model recorded Average Precision (AP) scores of 72.7%, 65.0%, 85.7%, and 64.6%, respectively. These metrics represent consistent improvements of 1.7% to 2.3% over the MambaYOLO baseline, underscoring the system’s potential for high-fidelity diagnostic assistance in real-time clinical workflows.