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    Biomedical image analysis: Segmentation, datasets, metrics and loss functions

    • Post author:N S Punn
    • Post published:July 24, 2021
    • Post category:My work/Tutorial
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    Biomedical image analysis The success of deep learning in image analysis has encouraged the biomedical imaging researchers to investigate its potential in analyzing various medical modalities to aid clinicians in…

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    Optimizers, Learning Rates and Callbacks

    • Post author:N S Punn
    • Post published:July 13, 2021
    • Post category:Tutorial
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    What is covered? Types of optimizers and learning ratesKeras callbacks and checkpoints like early stopping, adjusting learning rates, etc. Types of optimizers and adaptive learning methods Optimizers In neural networks…

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    Overview of Deep Learning Basics – II

    • Post author:N S Punn
    • Post published:April 9, 2021
    • Post category:Tutorial
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    Convolution Neural Network OverviewConvolution layerActivation layerPooling layer Overview Among the various deep learning models such as stacked auto-encoders [1], deep Boltzmann machines [2], deep conventional extreme learning machines [3], deep…

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    Overview of Deep Learning Basics – I

    • Post author:N S Punn
    • Post published:April 5, 2021
    • Post category:Tutorial
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    What is covered? IntroductionPerceptronNeural networkActivation functionLoss functionGradient descentSummaryRecommended resources Prologue Deep learning is a form of machine learning that uses a model of computing that mimics the structure of the…

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    • BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins
    • RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging
    • Glimpse of 8th Heidelberg Laureate Forum 2021
    • Distributed Deep Learning with Elephas
    • Biomedical image analysis: Segmentation, datasets, metrics and loss functions

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