Faculty Publications

Now showing 1 - 3 of 3

Computational methods for automated mitosis detection in histopathology images: A review
(Elsevier Sp. z o.o., 2021) Mathew, T.; Kini, J.R.; Rajan, J.
Mitosis detection is an important step in pathology procedures in the context of cancer diagnosis and prognosis. Prevalent process for this task is by manually observing Hematoxylin and Eosin (H & E) stained histopathology sections on glass slides through a microscope by trained pathologists. This conventional approach is tedious, error-prone, and has shown high inter-observer variability. With the advancement of computational technologies, automating mitosis detection by the use of image processing algorithms has attracted significant research interest. In the past decade, several methods appeared in the literature, addressing this problem and they have shown encouraging incremental progress towards a clinically usable solution. Mitosis count is an important parameter in grading of breast cancer and glioma, unlike other cancer types. Driven by the availability of multiple public datasets and open contests, most of the methods in literature address mitosis detection in breast cancer images. This paper is a comprehensive review of the methods published in the area of automated mitotic cell detection in H & E stained histopathology images of breast cancer in the last 10 years. We also discuss the current trends and future prospects of this clinically relevant task, augmenting humanity's fight against cancer. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
A novel deep classifier framework for automated molecular subtyping of breast carcinoma using immunohistochemistry image analysis
(Elsevier Ltd, 2022) Mathew, T.; Niyas, S.; Johnpaul, C.I.; Kini, J.; Rajan, J.
Breast carcinoma has various subtypes based on the genetic factors involved in the pathogenesis of the malignancy. Identifying the exact subtype and providing targeted treatment to the patient can improve the survival chances. Molecular subtyping through immunohistochemistry analysis is a pathology procedure to determine the subtype of breast cancer. The existing manual procedure is tedious and involves assessing the status of the four vital molecular biomarkers present in the tumor tissues. In this paper, a deep learning-based framework for automated molecular subtyping of breast cancer is proposed. Digital slide images of the four biomarkers are separately processed by the proposed framework. In the preprocessing stage, the non-informative background regions from the images are separated. The patches extracted from the foreground regions are classified into target classes using convolutional neural network models trained for this purpose. Classification results are post-processed to predict the status of all the four biomarkers. The predictions for the individual biomarkers are finally consolidated as per clinical guidelines to determine the subtype of the cancer. The proposed system is evaluated for the performance of individual biomarker status prediction and patient-level subtype classification.For patient-level evaluation of biomarkers ER, PR, K67, and HER2, the proposed method gives F1 Scores 1.00, 1.00, 0.90, and 0.94 respectively, whereas for molecular subtyping an F1 score of 0.89 is obtained. In both these aspects, the proposed framework has given significant results that show the effectiveness of our approach. © 2022 Elsevier Ltd
A deep learning based classifier framework for automated nuclear atypia scoring of breast carcinoma
(Elsevier Ltd, 2023) Mathew, T.; Johnpaul, C.I.; Ajith, B.; Kini, J.R.; Rajan, J.
Nuclear atypia scoring is an essential procedure in the grading of breast carcinoma. Manual procedure of nuclear atypia scoring is error-prone, and marked by pathologists’ disagreement and low reproducibility. Automated methods are actively attempted by researchers to solve the problems of manual scoring. In this work, we propose a novel deep learning-based framework for automated nuclear atypia scoring of breast cancer from histopathology slide images. The framework consists of three major phases namely preprocessing, deep learning, and postprocessing. The original three-class problem of atypia scoring at slide level is not suitable for direct application of deep learning algorithms. This is due to the large dimensions and structural complexity of slide images, compounded by the small sample size of the available dataset. Redesign of this problem into a six-class nuclei classification problem through a set of preprocessing steps to facilitate effective use of deep learning algorithms, and the flexibility of the proposed three-phase framework to use different algorithms in each phase are the novel aspects of the proposed work. We used the publicly available slide image dataset MITOS-ATYPIA that contains 600 slide images of high spatial dimension for the experiments. A five-fold cross validation with the train-test sample ratio 80:20 in each fold is used for the performance evaluation. The performance of the method based on this framework exceeds the state-of-the-art with the results 0.8766, 0.8760, and 0.8745 for the metrics precision, recall, and F1 score respectively. © 2023 Elsevier Ltd

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