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Related Experiment Video

Updated: Jul 4, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

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

Xiao Qin1, Quanmei Qian2, Xiaosen Li3

  • 1Guangxi Key Laboratory of Human-Computer Interaction and Intelligent Decision, Nanning Normal University, Nanning, Guangxi, 530100, China. 110027@nnnu.edu.cn.

Scientific Reports
|March 28, 2026
PubMed
Summary

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Introduction to Scalers01:21

Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
Scalar...

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ScaleMamba-YOLO enhances medical object detection by addressing scale variations and background noise. This new framework improves lesion identification accuracy for better clinical diagnostic assistance.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automated lesion detection faces challenges from diverse pathological scales and anatomical interference.
  • Standard Mamba-based detectors have limitations like fixed receptive fields and background signal leakage.

Purpose of the Study:

  • To introduce ScaleMamba-YOLO, an advanced medical object detection framework.
  • To overcome limitations of existing detectors by integrating selective state-space modeling and adaptive local feature refinement.

Main Methods:

  • Developed the Medical Multi-scale Local Feature Enhancement Block (MMLFE-Block) with heterogeneous convolutional kernels for hierarchical perception.
  • Integrated a Partial-Enhanced C2F (PEC2F) module using partial convolution (PConv) to refine feature aggregation and filter background noise.

Related Experiment Videos

Last Updated: Jul 4, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.7K

Main Results:

  • ScaleMamba-YOLO achieved high Average Precision (AP) scores: 72.7% (Br35H), 65.0% (BCCD), 85.7% (PLoPy), and 64.6% (VOC0712).
  • Demonstrated consistent performance improvements of 1.7% to 2.3% over the MambaYOLO baseline across datasets.

Conclusions:

  • ScaleMamba-YOLO effectively addresses scale variations and background interference in medical object detection.
  • The framework shows significant potential for high-fidelity diagnostic assistance in real-time clinical settings.