Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches

Nasrin, Mst Shamima
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676

Abstract Details

2021, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
Artificial intelligence (AI) based analysis is accelerating clinical diagnosis from pathological images and automating image analysis efficiently and accurately. Recently, Deep Learning (DL) algorithms have shown superior performance in pathological image analysis, such as tumor region identification, metastasis detection, and patient prognosis. As digital pathology becomes popular, it is crucial to evaluate the performance of DL approaches that show the best performance for the different color-space representations of pathological images. The main goal of this research is to analyze several supervised and unsupervised DL approaches in pathological image analysis. In this study, the Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model has been examined in six different color spaces (RGB, CIE, HSB, YCrCb, Lab, and HSL) pathological images and evaluate the best color space for tissue classification tasks. In addition, the Recurrent Residual U-Net (R2U-Net) model is evaluated in six different color spaces images in nuclei segmentation tasks and selects the best color space. Also, R2U-Net based autoencoder models are examined for medical image denoising such as digital pathology, dermoscopy, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). The performance of the R2U-Net based auto-encoder model is also evaluated for the Transfer domain (TD) between MRI and CT scan images. Finally, as pathological images have higher dimensions, it is necessary to reduce the dimensionality for analyzing these samples by obtaining its original features representation in the lower dimensions. In this research, DL features have been extracted, and then the t-distributed Stochastic Non-linear Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) are applied for clustering and visualization of pathological images.
Tarek M Taha (Advisor)
116 p.
Artificial Intelligence; Biomedical Research; Computer Engineering; Computer Science; Medical Imaging
IRRCNN; R2UNet; UMAP; tSNE; digital pathology; medical imaging

Recommended Citations

Citations

  • Nasrin, M. S. (2021). Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676

    APA Style (7th edition)

  • Nasrin, Mst Shamima. Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches . 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.

    MLA Style (8th edition)

  • Nasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches ." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676

    Chicago Manual of Style (17th edition)

dayton1620052562772676
1,351
© 2021, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.