Advisors
Now showing items 1-20 of 22
-
Anatomic context-aware segmentation of organs-at-risk in thorax computed tomography scans
(Bilkent University, 2022-12)Organ segmentation plays a crucial role in disease diagnosis and radiation therapy planning. Efficient and automated segmentation of the organs-at-risk (OARs) re-quires immediate attention since manual segmentation is a ... -
Automated cell analysis in microscopy images
(Bilkent University, 2018-09)High-throughput microscopy systems have become popular recently, which facilitate to acquire boundless microscopy images without requiring human intervention. However, the analysis of such amount of images using conventional ... -
Boosting fully convolutional networks for gland instance segmentation in histopathological images
(Bilkent University, 2019-08)In the current literature, fully convolutional neural networks (FCNs) are the most preferred architectures for dense prediction tasks, including gland segmentation. However, a signi cant challenge is to adequately train ... -
Color graph representation for structural analysis of tissue images
(Bilkent University, 2010)Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may ... -
Constrained Delaunay triangulation for diagnosis and grading of colon cancer
(Bilkent University, 2009)In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer ... -
Deep convolutional network for tumor bud detection
(Bilkent University, 2019-04)The existence of tumor buds is accepted as a promising biomarker for staging colorectal carcinomas. In the current practice of medicine, these tumor buds are detected by the manual examination of a immunohistochemically ... -
Deep learning based cell segmentation in histopathological images
(Bilkent University, 2018-08)In digital pathology, cell imaging systems allow us to comprehend histopathological events at the cellular level. The first step in these systems is generally cell segmentation, which substantially affects the subsequent ... -
Deep learning based unsupervised tissue segmentation in histopathological images
(Bilkent University, 2017-11)In the current practice of medicine, histopathological examination of tissues is essential for cancer diagnosis. However, this task is both subject to observer variability and time consuming for pathologists. Thus, it ... -
Deep learning for digital pathology
(Bilkent University, 2020-11)Histopathological examination is today’s gold standard for cancer diagnosis and grading. However, this task is time consuming and prone to errors as it requires detailed visual inspection and interpretation of a ... -
FourierNet: shape-preserving network for henle’s fiber layer segmentation in optical coherence tomography images
(Bilkent University, 2022-09)Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Müller cell ... -
Histopathological image classification using salient point patterns
(Bilkent University, 2011)Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer ... -
Local object patterns for tissue image representation and cancer classification
(Bilkent University, 2013)Histopathological examination of a tissue is the routine practice for diagnosis and grading of cancer. However, this examination is subjective since it requires visual interpretation of a pathologist, which mainly depends ... -
Multi-task network for computed tomography segmentation through fractal dimension estimation
(Bilkent University, 2023-01)Multi-task learning proved to be an effective strategy to increase the performance of a dense prediction network on a segmentation task, by defining auxiliary tasks to reflect different aspects of the problem and ... -
Object-oriented testure analysis and unsupervised segmentation for histopathological images
(Bilkent University, 2012)The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual ... -
Perceptual watersheds for cell segmentation in fluorescence microscopy images
(Bilkent University, 2012)High content screening aims to analyze complex biological systems and collect quantitative data via automated microscopy imaging to improve the quality of molecular cellular biology research in means of speed and accuracy. ... -
Qualitative test-cost sensitive classification
(Bilkent University, 2008)Decision making is a procedure for selecting the best action among several alternatives. In many real-world problems, decision has to be taken under the circumstances in which one has to pay to acquire information. In ... -
Resampling-based Markovian modeling for automated cancer diagnosis
(Bilkent University, 2011)Correct diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This ... -
Rule based segmentation of colon glands
(Bilkent University, 2018-09)Colon adenocarcinoma, which accounts for more than 90 percent of all colorectal cancers, originates from epithelial cells that form colon glands. Thus, for its diagnosis and grading, it is important to examine the ... -
Segmentation of colon glands by object graphs
(Bilkent University, 2008)Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in ... -
Shape-preserving loss in deep learning for cell segmentation
(Bilkent University, 2020-07)Fully convolutional networks (FCNs) have become the state-of-the-art models for cell instance segmentation in microscopy images. These networks are trained by minimizing a loss function, which typically defines the loss of ...
