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Semantic-Oriented Labeled-to-Unlabeled Distribution Translation for Image Segmentation.

Xiaoqing Guo, Jie Liu, Yixuan Yuan

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    |September 20, 2021
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    This study introduces a new framework for medical image segmentation, improving accuracy by learning from both labeled and unlabeled data. The Semantic-oriented Contrastive Learning (SoCL) and Labeled-to-unlabeled Distribution Translation (L2uDT) methods address data scarcity and bias for better disease diagnosis and treatment planning.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Medical image segmentation is vital for diagnosis and treatment planning.
    • Current deep learning models struggle with pixel-wise classification and semantic correlations across images.
    • Limited and biased pixel-wise annotated medical data hinders supervised learning.

    Purpose of the Study:

    • To propose a novel framework, Labeled-to-unlabeled Distribution Translation (L2uDT) with Semantic-oriented Contrastive Learning (SoCL), for medical image segmentation.
    • To address challenges of vague feature distribution and scarce, biased annotated data in medical imaging.
    • To enhance the performance of medical image segmentation models.

    Main Methods:

    • Semantic-oriented Contrastive Learning (SoCL) clusters pixels into semantically coherent groups, learning a feature space with intra-class compactness and inter-class separability.
    • A Labeled-to-unlabeled Distribution Translation (L2uDT) strategy approximates desired data distribution using unlabeled data.
    • A bias estimator and gradual-paced shift progressively translate labeled data distribution towards unlabeled data distribution for unbiased optimization.

    Main Results:

    • The proposed L2uDT framework with SoCL achieves state-of-the-art performance on benchmark datasets (EndoScene and PROSTATEx).
    • The method effectively learns from limited labeled data by leveraging extensive unlabeled data.
    • Demonstrates significant improvements in medical image segmentation accuracy.

    Conclusions:

    • The L2uDT framework with SoCL offers an effective solution for medical image segmentation, overcoming limitations of existing methods.
    • This approach enhances feature representation and mitigates issues related to data scarcity and distribution bias.
    • The method shows strong potential for advancing automated medical image analysis in clinical applications.