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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Updated: Oct 3, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities.

Xiangmin Han, Xiaoyan Fei, Jun Wang

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    |February 16, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel doubly supervised transfer classifier (DSTC) to improve medical image diagnosis accuracy. DSTC effectively utilizes limited labeled data for better knowledge transfer in computer-aided diagnosis systems.

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    Area of Science:

    • Medical Imaging Analysis
    • Machine Learning in Healthcare
    • Computer-Aided Diagnosis (CAD)

    Background:

    • Transfer learning (TL) enhances single-modal-imaging-based CAD by leveraging knowledge from related modalities, addressing small-sample-size issues.
    • Existing TL methods struggle with limited source domain data and underutilize labeled samples for improved knowledge transfer in medical imaging.

    Purpose of the Study:

    • To propose a novel doubly supervised transfer classifier (DSTC) algorithm to overcome limitations in current TL-based CAD.
    • To enhance knowledge transfer by fully utilizing shared and unpaired labeled data in medical imaging datasets.

    Main Methods:

    • Developed the DSTC algorithm, integrating Support Vector Machine plus (SVM+) classifier and Low-Rank Representation (LRR).
    • SVM+ guides knowledge transfer using shared labels for paired data.
    • LRR, specifically block-diagonal low-rank (BLR) with Schatten-p norm, performs supervised TL on unpaired data.

    Main Results:

    • DSTC effectively integrates labeled and unlabeled data for improved transfer learning.
    • Evaluated on Alzheimer's Disease Neuroimaging Initiative (ADNI) and Bimodal Breast Ultrasound Image (BBUI) datasets.
    • Experimental results demonstrated the significant effectiveness of the DSTC algorithm.

    Conclusions:

    • The proposed DSTC algorithm offers a robust solution for enhancing TL in CAD systems.
    • DSTC successfully addresses challenges of limited data and suboptimal label utilization in medical imaging.
    • This approach shows promise for improving diagnostic accuracy in various medical imaging applications.