Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Mathew, T.

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Study and analysis of various task scheduling algorithms in the cloud computing environment
    (Institute of Electrical and Electronics Engineers Inc., 2014) Mathew, T.; Chandra Sekaran, K.C.; Jose, J.
    Cloud computing is a novel perspective for large scale distributed computing and parallel processing. It provides computing as a utility service on a pay per use basis. The performance and efficiency of cloud computing services always depends upon the performance of the user tasks submitted to the cloud system. Scheduling of the user tasks plays significant role in improving performance of the cloud services. Task scheduling is one of the main types of scheduling performed. This paper presents a detailed study of various task scheduling methods existing for the cloud environment. A brief analysis of various scheduling parameters considered in these methods is also discussed in this paper. © 2014 IEEE.
  • No Thumbnail Available
    Item
    Automated Molecular Subtyping of Breast Cancer Through Immunohistochemistry Image Analysis
    (Springer Science and Business Media Deutschland GmbH, 2023) Niyas, S.; Priya, S.; Oswal, R.; Mathew, T.; Kini, J.R.; Rajan, J.
    Molecular subtyping has a significant role in cancer prognosis and targeted therapy. However, the prevalent manual procedure for this has disadvantages, such as deficit of medical experts, inter-observer variability, and high time consumption. This paper suggests a novel approach to automate molecular subtyping of breast cancer using an end-to-end deep learning model. Immunohistochemistry (IHC) images of the tumor tissues are analyzed using a three-stage system to determine the subtype. A modified Res-UNet CNN architecture is used in the first stage to segregate the biomarker responses. This is followed by using a CNN classifier to determine the status of the four biomarkers. Finally, the biomarker statuses are combined to determine the specific subtype of breast cancer. For each IHC biomarker, the performance of segmentation models is analyzed qualitatively and quantitatively. In addition, the patient-level biomarker prediction results are also assessed. The findings of the suggested technique demonstrate the potential of computer-aided techniques to diagnose the subtypes of breast cancer. The proposed automated molecular subtyping approach can accelerate pathology procedures, considerably reduce pathologists’ workload, and minimize the overall cost and time required for diagnosis and treatment planning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.