Whole Slide Images Based Cervical Cancer Classification Using Self-Supervised Learning and Multiple Instance Learning

Project Code :TMPGAI94

Objective

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.

Abstract

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.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software: Matlab 2020a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

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:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • 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:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    • Morphological processing
    • Segmentation etc.,
  • How to extend our work to another real time applications
  • Project development Skills
    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills 
    • Thesis writing skills

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