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Related Concept Videos

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Related Experiment Video

Updated: Aug 29, 2025

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Towards bridging the distribution gap: Instance to Prototype Earth Mover's Distance for distribution alignment.

Qin Zhou1, Runze Wang1, Guodong Zeng2

  • 1Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, 200240 Shanghai, China.

Medical Image Analysis
|September 8, 2022
PubMed
Summary

This study introduces Instance to Prototype Earth Mover's Distance (I2PEMD) to address distribution divergence in medical image analysis. I2PEMD effectively aligns data distributions, improving deep learning performance in segmentation and classification tasks.

Keywords:
Distribution alignmentEarth Mover’s DistanceInstance to prototype matchingSemi-supervised classificationUnpaired multi-modal segmentation

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Area of Science:

  • Medical Image Analysis
  • Deep Learning
  • Computer Vision

Background:

  • Distribution divergence poses a significant challenge in deep learning for medical image analysis, hindering performance across domains and feature subspaces.
  • Existing methods struggle to effectively bridge the distribution gap, limiting knowledge transfer and model generalization.
  • Addressing this gap is crucial for advancing medical AI applications.

Purpose of the Study:

  • To propose a novel module, Instance to Prototype Earth Mover's Distance (I2PEMD), for effective distribution alignment in medical image analysis.
  • To enhance deep learning model performance by narrowing distribution gaps across different domains and feature subspaces.
  • To validate the efficacy of I2PEMD on diverse medical imaging tasks.

Main Methods:

  • Developed Instance to Prototype Earth Mover's Distance (I2PEMD) module for distribution alignment.
  • Learned shared class-specific prototypes to progressively narrow distribution gaps.
  • Utilized Earth Mover's Distance (EMD) to incorporate cross-class relationships during embedding alignment.
  • Applied I2PEMD as a regularization term in multi-modal medical image segmentation.
  • Employed I2PEMD as an alignment measure for semi-supervised classification data selection.

Main Results:

  • Demonstrated significant improvements in multi-modal medical image segmentation through I2PEMD regularization.
  • Showcased enhanced accuracy and robustness in semi-supervised classification via I2PEMD-guided pseudo-labeling.
  • Comprehensive experiments confirmed the effectiveness of I2PEMD in aligning data distributions.
  • The proposed method successfully mitigated the negative impact of distribution divergence.

Conclusions:

  • Instance to Prototype Earth Mover's Distance (I2PEMD) is an effective method for distribution alignment in medical image analysis.
  • I2PEMD enhances deep learning performance in both segmentation and semi-supervised classification tasks.
  • The proposed approach offers a promising solution for improving generalization and knowledge transfer in medical AI.