Also Available Domains Image Detection
In this paper, we propose to combine self-supervised learning with multiple instances learning to deal with large WSIs datasets only with the reported diagnoses as labels.
Most whole-slide picture classification systems now rely on manual pixel-level annotations, which are delicate and time-consuming, and necessitate the annotation of specialized topic expertise. We propose employing self-supervised learning and multiple instances learning to handle large WSI datasets with only the reported diagnoses as labels to address this issue. Here we use a machine learning technique i.e. K-Nearest Neighbors (KNN) and the deep neural network i.e., convolutional neural network that showed better performance when compared to KNN and the features learned by CNN are better for classification applications.
Keywords: Whole Slide Images, KNN algorithm, Convolutional neural network, Features, Cervical Cancer.
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Software: Matlab 2020a or above
Hardware:
Operating Systems:
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
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RAM:
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Recommended: 8 GB
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
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