Browsing by Author "Ozyer, Tansel"

Now showing 1 - 5 of 5
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    BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis
    (2019-02-12) Aksac, Alper; Demetrick, Douglas J; Ozyer, Tansel; Alhajj, Reda
    Abstract Objectives Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome. Data description This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images.
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    CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology
    (2020-01-06) Aksac, Alper; Ozyer, Tansel; Demetrick, Douglas J; Alhajj, Reda
    Abstract Objective Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results CACTUS has been evaluated using a surgical pathology application—the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.
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    Offline and Online Interactive Frameworks for MRI and CT Image Analysis in the Healthcare Domain : The Case of COVID-19, Brain Tumors and Pancreatic Tumors
    (2023-08) Sailunaz, Kashfia; Alhajj, Reda S.; Alhajj, Reda S.; Rokne, Jon George; Ozyer, Tansel; Kawash, Jalal Yusef; Agarwal, Nitin
    Medical imaging represents the organs, tissues and structures underneath the outer layers of skin and bones etc. and stores information on normal anatomical structures for abnormality detection and diagnosis. In this thesis, tools and techniques are used to automate the analysis of medical images, emphasizing the detection of brain tumor anomalies from brain MRIs, Covid infections from lung CT images and pancreatic tumor from pancreatic CT images. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning models, and recent deep learning models are used. The following problems for medical images : abnormality detection, abnormal region segmentation, interactive user interface to represent the results of detection and segmentation while receiving feedbacks from healthcare professionals to improve the analysis procedure, and finally report generation, are considered in this research. Complete interactive systems containing conventional models, machine learning, and deep learning methods for different types of medical abnormalities have been proposed and developed in this thesis. The experimental results show promising outcomes that has led to the incorporation of the methods for the proposed solutions based on the observations of the performance metrics and their comparisons. Although currently separate systems have been developed for brain tumor, Covid and pancreatic cancer, the success of the developed systems show a promising potential to combine them to form a generalized system for analyzing medical imaging of different types collected from any organs to detect any type of abnormalities.
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    Social Media Emergency Analysis and Realistic Evacuation Modeling
    (2021-09-07) Sahin, Coskun; Alhajj, Reda; Alhajj, Reda; Ozyer, Tansel; Rokne, Jon George; Ruhe, Guenther; Mouhoub, Malek
    The widespread of disasters necessitates appropriate actions to be taken to avoid casualties or at least reduce them to the minimum level possible. In this work, we propose solutions to two of the ways we can help it. The first part is using the potential of social media to detect emergencies and provide further information for the public and rescue teams. It uses a multi-layer machine learning approach to find and cluster emergency-related messages. Natural language processing and information extraction techniques are adopted for location detection, casualty and severity calculation. The effectiveness of the model is shown on Twitter data in nearly real-time using its own API. The second part of the thesis investigates the area of crowd behavior modeling in order to provide a platform for simulating various emergency evacuation scenarios. Crowd behavior modeling is an important challenge especially for games, social simulation and military training software. There are various applications focusing on specific approaches to solve social, physical, emotional and cognitive dimensions of this problem. In this work, first, we built and agent-based framework that adopts OCC emotion model and Belief-Desire-Intention (BDI) approach in a heterogeneous environment containing individuals with different knowledge of the surroundings. It uses a deep Q-learning model running on top of a neural network for creating a set of partially-trained agents. Second, we simulate group decision-making process using a mathematical ferromagnetism model and analyze how it affects the overall success of the crowd achieving a goal together. Our work contributes to the literature in two different ways. Firstly, it shows that a multi-layer classification model is effective for detecting emergencies in real-time using unstructured data. Moreover, it provides evacuation simulation scenarios on the public buildings where an emergency occurs and saves valuable time for rescue teams while planning an emergency response. Secondly, it combines state-of-the-art frameworks for crowd behavior modeling with a partial learning mechanism. Our experiments show that the system is capable of simulating common group patterns during emergencies. Moreover, it provides a generic crowd framework, where partially-trained agents can find optimal solutions via interaction and collaboration.