Conference Papers

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    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.
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    Estimation of Breast Tumor Parameters by Random Forest Method with the Help of Temperature Data on the Surface of the Numerical Breast Model
    (Springer Science and Business Media Deutschland GmbH, 2023) Venkatapathy, G.; Rahul, V.M.; Gnanasekaran, N.
    The second most frequent reason for cancer-related fatalities in women is breast cancer. When a condition is identified early, better treatment choices are available. Different temperature patterns are seen on the breast surface due to the tumors, which change blood perfusion rate and metabolic heat production. Thermography is an infrared imaging technology for breast cancer screening that records temperature variations. The temperature dataset on the surface of the breast that corresponded to the tumor’s diameter and the location was needed for the current study, but such actual data are not accessible. Thus, the modeling and development of a dataset constitute the initial component of the current study. The bio-heat transport equation is solved using COMSOL multiphysics software, and the model consists of a spherical tumor inside of a hemispherical breast model. By changing the sizes and positions of the tumor inside the breast during simulations, a reliable dataset is created. The training and testing of the dataset produced from the simulations using the random forest method make up the second portion of the current study. Breast skin temperature is used as an input in a random forest machine learning algorithm in the current work to determine the diameter and location of the tumor inside the breast. The diameter and area of the tumor location are estimated by a trained random forest algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.