Faculty Publications
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Item 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 SciencesItem In vivo anticancer and histopathology studies of Schiff bases on Ehrlich ascitic carcinoma cells. 1st Cancer Update.(2013) Dhanya, D.; Isloor, A.M.; Shetty, P.; Nayak, P.G.; Pai, K.S.R.Three Schiff bases in two different concentrations were evaluated for their anti-tumor activity against Ehrlich ascites carcinoma (EAC) bearing Swiss albino mice. The in vivo anti-tumor potency of Schiff bases was assessed by measuring the increase in mean survival time of the drug treated over untreated control mice and treated standard (cisplatin) mice. Their toxicity was assessed in vivo in normal, standard, and EAC-bearing mice by measuring the drug-induced changes in biochemical as well as hematological parameters. The histopathology studies to assess the toxicity of these compounds on vital organs also have been studied. Among the three Schiff bases studied, 4-({[3-(4-fluorophenyl)-1. H-pyrazol-4-yl]methylene}amino)-5-[(2-methylphenoxy)methyl]-1,2,4-triazole-3-thiol (SB-3) at an optimal dose of 100. mg/kg body weight was found to enhance the mean survival time of infected mice. Deviated hematological parameters and mean survival time in tumor bearing mice were found to be significantly restored towards normal after treatment with SB-3 100. mg/kg body weight of mice. The ALP and SGOT values were found to approach the normal range. A:G ratios also did not deviate from normal on treatment with SB-3. The histopathology studies revealed only mild hepatotoxicity and nephrotoxicity when compared to the normal and standard. The splenic cellularity also did not show much variation from normal. SB-3 at a prime dose of 100. mg has shown promising anticancer activity in vivo against EAC when compared to standard drug with minimum toxic effects. © 2010 .Item 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 LtdItem 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 LtdItem A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images(Institute of Electrical and Electronics Engineers Inc., 2024) Pateel, G.P.; Senapati, K.; Pandey, A.K.The Human Epidermal Growth Factor Receptor 2 (HER2) is one of the aggressive subtypes of breast cancer. The HER2 status decides the requirement of breast cancer patients to receive HER2-targeted therapy. The HER2 testing involves combining Immunohistochemistry (IHC) screening, followed by fluorescence in situ hybridization (FISH) for cases where IHC results are equivocal. These tests may involve multiple trials, are time intensive, and tend to be more expensive for certain classes of people. Hematoxylin and Eosin (HE) staining is employed for visualizing general tissue morphology and is a routine, cost-effective method. In this study, we introduce a novel automated class-wise weighted average ensemble deep learning algorithm at the decision level. The proposed algorithm fuses three pre-trained deep-learning models at the decision level by assigning a weight to each class based on their performance of the model to classify the HE-stained breast histopathology images into multi-class HER2 statuses as HER2-0+, HER2-1+, HER2-2+, and HER2-3+. The class-wise weight allocation to the base classifiers is one of the key features of the proposed algorithm. The presented framework surpasses all the existing methods currently employed on the Breast Cancer Immunohistochemistry (BCI) dataset, establishing itself as a dependable approach for detecting HER2 status from HE-stained images. This study highlights the robustness of the proposed algorithm as well as the sufficient information encapsulated within HE-stained images for the effective detection of the HER2 protein present in breast cancer. Therefore, the proposed method possesses the potential to sideline the need for IHC laboratory tests, which hoard time and money. © 2013 IEEE.