Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
101 results
Search Results
Publication Metadata only The role of endometrial sampling before Hysterectomy in premenopausal women with abnormal uterine bleeding(MDPI, 2024) Kuru, Oguzhan; Erkan, Ipek Betul Ozcivit; Saricoban, Cansu Turker; Akgor, Utku; Ilvan, Sennur; Department of Computer Engineering; İnan, Neslihan Gökmen; Department of Computer Engineering; College of EngineeringBackground/Objectives: An endometrial sampling is recommended for patients experiencing abnormal uterine bleeding above the age of 40 or 45. Valid risk prediction models are needed to accurately assess the risk of endometrial cancer and avoid an unnecessary endometrial biopsy in premenopausal women. We aimed to assess the necessity and usefulness of preoperative endometrial sampling by evaluating premenopausal women who underwent hysterectomy for abnormal uterine bleeding after preoperative endometrial sampling at our clinic. Methods: A retrospective analysis was conducted on 339 patients who underwent preoperative endometrial sampling and subsequently underwent hysterectomy due to abnormal uterine bleeding. Detailed gynecologic examinations, patient histories, and reports of endometrial sampling and hysterectomy were recorded. Cohen's Kappa (kappa) statistic was utilized to evaluate the concordance between histopathological results from an endometrial biopsy and hysterectomy. Results: The mean age of the cohort was 47 +/- 4 years. Endometrial biopsies predominantly revealed benign findings, with 137 (40.4%) cases showing proliferative endometrium and 2 (0.6%) cases showing endometrial cancer. Following hysterectomy, final pathology indicated proliferative endometrium in 208 (61.4%) cases, with 7 (2.1%) cases showing endometrioid cancer. There was a statistically significant but low level of concordance between histopathological reports of endometrial biopsy and hysterectomy results (Kappa = 0.108; p < 0.001). Significant differences were observed only in the body mass index of patients based on hysterectomy results (p = 0.004). When demographic characteristics were compared with cancer incidence, smoking status and preoperative endometrial biopsy findings showed statistically significant differences (p = 0.042 and p = 0.010, respectively). Conclusions: The concordance between the pathological findings of a preoperative endometrial biopsy and hysterectomy is low. Body mass index is an important differentiating factor between benign histopathologic findings of endometrium and endometrial neoplasia. Moreover, adenomyosis was found to be associated with endometrial cancer cases. The current approach to premenopausal women with abnormal uterine bleeding, which includes a routine endometrial biopsy, warrants re-evaluation by international societies and experts.Publication Metadata only HiSEG: Human assisted instance segmentation(Elsevier Ltd, 2024) Department of Computer Engineering; Sezgin, Tevfik Metin; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and EngineeringInstance segmentation is a form of image detection which has a range of applications, such as object refinement, medical image analysis, and image/video editing, all of which demand a high degree of accuracy. However, this precision is often beyond the reach of what even state-of-the-art, fully automated instance segmentation algorithms can deliver. The performance gap becomes particularly prohibitive for small and complex objects. Practitioners typically resort to fully manual annotation, which can be a laborious process. In order to overcome this problem, we propose a novel approach to enable more precise predictions and generate higher-quality segmentation masks for high-curvature, complex and small-scale objects. Our human-assisted segmentation method, HiSEG, augments the existing Strong Mask R-CNN network to incorporate human-specified partial boundaries. We also present a dataset of hand-drawn partial object boundaries, which we refer to as “human attention maps”. In addition, the Partial Sketch Object Boundaries (PSOB) dataset contains hand-drawn partial object boundaries which represent curvatures of an object's ground truth mask with several pixels. Through extensive evaluation using the PSOB dataset, we show that HiSEG outperforms state-of-the art methods such as Mask R-CNN, Strong Mask R-CNN, Mask2Former, and Segment Anything, achieving respective increases of +42.0, +34.9, +29.9, and +13.4 points in APMask metrics for these four models. We hope that our novel approach will set a baseline for future human-aided deep learning models by combining fully automated and interactive instance segmentation architectures.Publication Metadata only Subspace-based techniques for retrieval of general 3D models(IEEE, 2009) Sankur, Bülent; Dutaǧac, Helin; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; 107907In this paper we investigate the potential of subspace techniques, such as, PCA, ICA and NMF in the case of indexing and retrieval of generic 3D models. We address the 3D shape alignment problem via continuous PCA and the exhaustive axis labeling and reflections. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. Our performance on the Princeton Shape Benchmark is on a par with the state-of-the-art methods. ©2009 IEEE.Publication Metadata only Affect burst detection using multi-modal cues(IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/ARecently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.Publication Metadata only Determination of the correspondence between mobility (rigidity) and conservation of the interface residues(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AHot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.Publication Metadata only Artificial bandwidth extension of speech excitation(IEEE, 2015) Department of Computer Engineering; N/A; Erzin, Engin; Turan, Mehmet Ali Tuğtekin; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 34503; N/AIn this paper, a new approach that extends narrowband excitation signals to synthesize wide-band speech have been proposed. Bandwidth extension problem is analyzed using source-filter separation framework where a speech signal is decomposed into two independent components. For spectral envelope extension, our former work based on hidden Markov model have been used. For excitation signal extension, the proposed method moves the spectrum based on correlation analysis where the distance between the harmonics and the structure of the excitation signal are preserved in high-bands. In experimental studies, we also apply two other well-known extension techniques for excitation signals comparatively and evaluate the overall performance of proposed system using the PESQ metric. Our findings indicate that the proposed extension method outperforms other two techniques. © 2015 IEEE./ Öz: Bu çalışmada dar bantlı kaynak sinyallerinin bant genişliği artırılarak geniş bantlı konuşma sentezleyen yeni bir yaklaşım önerilmektedir. Bant genişletme problemi kaynak süzgeç analizinin yardımıyla iki bağımsız bileşen üzerinde ayrı ayrı ele alınmıştır. Süzgeç yapısını şekillendiren izgesel zarfı, saklı Markov modeli tabanlı geçmiş çalışmamızı kullanarak iyileştirirken, dar bantlı kaynak sinyalinin genişletilmesi için izgesel kopyalamaya dayalı yeni bir yöntem öneriyoruz. Bu yeni yöntemde dar bantlı kaynak sinyalinin yüksek frekans bileşenlerindeki harmonik yapısını, ilinti analizi ile genişletip geniş bantlı kaynak sinyali sentezlemekteyiz. Öne sürülen bu iyileştirmenin başarımını ölçebilmek için literatürde sıklıkla kullanılan iki ayrı genişletme yöntemi de karşılaştırmalı olarak degerlendirilmekte- dir. Deneysel çalışmalarda öne sürdüğümüz genişletmenin PESQ ölçütüyle nesnel başarımı gösterilmiştir.Publication Metadata only RegMT system for machine translation, system combination, and evaluation(Association for Computational Linguistics, 2011) Department of Computer Engineering; Yüret, Deniz; Biçici, Ergün; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/AWe present the results we obtain using our RegMT system, which uses transductive regression techniques to learn mappings between source and target features of given parallel corpora and use these mappings to generate machine translation outputs. Our training instance selection methods perform feature decay for proper selection of training instances, which plays an important role to learn correct feature mappings. RegMT uses L2 regularized regression as well as L1 regularized regression for sparse regression estimation of target features. We present translation results using our training instance selection methods, translation results using graph decoding, system combination results with RegMT, and performance evaluation with the F1 measure over target features as a metric for evaluating translation quality.Publication Metadata only Towards verifying eventually consistent applications(Association for Computing Machinery, 2014) N/A; N/A; N/A; Department of Computer Engineering; Özkan, Burcu Külahcıoğlu; Mutlu, Erdal; Taşıran, Serdar; PhD Student; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/AN/APublication Metadata only Run-time verification of optimistic concurrency(Springer, 2010) Qadeer, Shaz; N/A; Department of Computer Engineering; Department of Computer Engineering; Sezgin, Ali; Taşıran, Serdar; Muşlu, Kıvanç; Researcher; Faculty Member; Undergraduate Student; Department of Computer Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; N/AAssertion based specifications are not suitable for optimistic concurrency where concurrent operations are performed assuming no conflict among threads and correctness is cast in terms of the absence or presence of conflicts that happen in the future. What is needed is a formalism that allows expressing constraints about the future. In previous work, we introduced tressa claims and incorporated prophecy variables as one such formalism. We investigated static verification of tressa claims and how tressa claims improve reduction proofs. In this paper, we consider tressa claims in the run-time verification of optimistic concurrency implementations. We formalize, via a simple grammar, the annotation of a program with tressa claims. Our method relieves the user from dealing with explicit manipulation of prophecy variables. We demonstrate the use of tressa claims in expressing complex properties with simple syntax. We develop a run-time verification framework which enables the user to evaluate the correctness of tressa claims. To this end, we first describe the algorithms for monitor synthesis which can be used to evaluate the satisfaction of a tressa claim over a given execution. We then describe our tool implementing these algorithms. We report our initial test results.Publication Metadata only KU: word sense disambiguation by substitution(Association for Computational Linguistics, 2007) Department of Computer Engineering; Yüret, Deniz; Faculty Member; Department of Computer Engineering; College of Engineering; 179996Data sparsity is one of the main factors that make word sense disambiguation (WSD) difficult. To overcome this problem we need to find effective ways to use resources other than sense labeled data. In this paper I describe a WSD system that uses a statistical language model based on a large unannotated corpus. The model is used to evaluate the likelihood of various substitutes for a word in a given context. These likelihoods are then used to determine the best sense for the word in novel contexts. The resulting system participated in three tasks in the SemEval 2007 workshop. The WSD of prepositions task proved to be challenging for the system, possibly illustrating some of its limitations: e.g. not all words have good substitutes. The system achieved promising results for the English lexical sample and English lexical substitution tasks.