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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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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 Factors affecting Turkish medical students' pursuit of a career in neurosurgery: a single center survey study(Elsevier Inc., 2024) Çalış, Fatih; Şimşek, Abdullah Talha; Topyalın, Nur; Adam, Baha E.; Elias, Çimen; Aksu, Muhammed Emin; Aladdam, Mohammed; Gültekin, Güliz; Sorkun, Muhammet Hüseyin; Tez, Müjgan; Balak, Naci; Department of Computer Engineering; İnan, Neslihan Gökmen; Department of Computer Engineering; College of EngineeringBackground: Statistics show that over the past 2 decades, even in high-income countries, fewer and fewer students have listed neurosurgery as their top career option. Literature on medical students' pursuit of neurosurgical careers in middle- and low-income countries are scarce. The aim of this research, conducted in Turkey with a middle-income economy, was to contribute insights relevant to medical education and neurosurgery across the world. Methods: A survey was conducted with a target sample of fourth-year (167 students), fifth-year (169 students), and sixth-year (140 students) medical students (476 in total) from the Medical School at Istanbul Medeniyet University in Turkey. The response rates of the fourth-, fifth-, and sixth-year students were 62% (104/167), 53% (90/169), and 50% (70/140), respectively (in total, 266, including 147 female and 119 male). Results: In terms of the genuine intention, only 2.5% of men and 2.7% of women were committed to specializing in neurosurgery. This study further revealed that possible reasons for these students' low motivation to specialize in neurosurgery were their beliefs that in neurosurgery, the physical and psychological demands were high, and the night shifts were intense, meaning they would not have a social life or spare time for their hobbies; that morbidity/mortality were high; and that financial incentives were insufficient, especially in public institutions. Conclusion: Turkish medical students did not rank neurosurgery at the top of their career choices. Possible reasons for this are socioeconomic factors and the inadequate introduction of neurosurgery to medical students. © 2024 Elsevier Inc.Publication Metadata only FractalRG: advanced fractal region growing using Gaussian mixture models for left atrium segmentation(Academic Press Inc Elsevier Science, 2024) Firouznia, Marjan; Koupaei, Javad Alikhani; Faez, Karim; Jabdaragh, Aziza Saber; Department of Computer Engineering; Demir, Çiğdem Gündüz; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of EngineeringThis paper presents an advanced region growing method for precise left atrium (LA) segmentation and estimation of atrial wall thickness in CT/MRI scans. The method leverages a Gaussian mixture model (GMM) and fractal dimension (FD) analysis in a three -step procedure to enhance segmentation accuracy. The first step employs GMM for seed initialization based on the probability distribution of image intensities. The second step utilizes fractal -based texture analysis to capture image self -similarity and texture complexity. An enhanced approach for generating 3D fractal maps is proposed, providing valuable texture information for region growing. In the last step, fractal -guided 3D region growing is applied for segmentation. This process expands seed points iteratively by adding neighboring voxels meeting specific similarity criteria. GMM estimations and fractal maps are used to restrict the region growing process, reducing the search space for global segmentation and enhancing computational efficiency. Experiments on a dataset of 10 CT scans with 3,947 images resulted in a Dice score of 0.85, demonstrating superiority over traditional techniques. In a dataset of 30 MRI scans with 3,600 images, the proposed method achieved a competitive Dice score of 0.89 +/- 0.02, comparable to Deep Learning -based models. These results highlight the effectiveness of our approach in accurately delineating the LA region across diverse imaging modalities.Publication Metadata only On the rate of convergence of a classifier based on a transformer encoder(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Gurevych, Iryna; Kohler, Michael; Department of Computer Engineering; Şahin, Gözde Gül; Faculty Member; Department of Computer Engineering; College of Engineering; 366984Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the a posteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between the Transformer classifiers theoretically analyzed in this paper and the ones used in practice today is illustrated by means of classification problems in natural language processing.Publication Metadata only Coarse-to-fine combinatorial matching for dense isometric shape correspondence(Wiley, 2011) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We present a dense correspondence method for isometric shapes, which is accurate yet computationally efficient. We minimize the isometric distortion directly in the 3D Euclidean space, i.e., in the domain where isometry is originally defined, by using a coarse-to-fine sampling and combinatorial matching algorithm. Our method does not require any initialization and aims to find an accurate solution in the minimum-distortion sense for perfectly isometric shapes. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes.Publication Metadata only Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes(Elsevier, 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Özbabacan, Saliha Ece Acuner; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; 264351Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.Publication Metadata only Special issue: international workshop on runtime verification 2007(Oxford University Press (OUP), 2010) Sokolsky, Oleg; Department of Computer Engineering; Taşıran, Serdar; Faculty Member; Department of Computer Engineering; College of Engineering; N/AN/APublication Metadata only Multimodal person recognition for human-vehicle interaction(IEEE Computer Society, 2006) Ercil, A; Erdogan, H; Abut, H; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Erzin, Engin; Yemez, Yücel; Tekalp, Ahmet Murat; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; College of Engineering; 34503; 107907; 26207Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies.Publication Metadata only TRAF3 signaling: Competitive binding and evolvability of adaptive viral molecular mimicry(Elsevier, 2016) Guven-Maiorov, Emine; VanWaes, Carter; Chen, Zhong; Tsai, Chung-Jung; Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; 26605; 8745Background: The tumor necrosis factor receptor (TNFR) associated factor 3 (TRAF3) is a key node in innate and adaptive immune signaling pathways. TRAF3 negatively regulates the activation of the canonical and non canonical NF-kappa B pathways and is one of the key proteins in antiviral immunity. Scope of Review: Here we provide a structural overview of TRAF3 signaling in terms of its competitive binding and consequences to the cellular network. For completion, we also include molecular mimicry of TRAF3 physiological partners by some viral proteins. Major Conclusions: By out-competing host partners, viral proteins aim to subvert TRAF3 antiviral action. Mechanistically, dynamic, competitive binding by the organism's own proteins and same-site adaptive pathogen mimicry follow the same conformational selection principles. General Significance: Our premise is that irrespective of the eliciting event - physiological or acquired pathogenic trait - pathway activation (or suppression) may embrace similar conformational principles. However, even though here we largely focus on competitive binding at a shared site, similar to physiological signaling other pathogen subversion mechanisms can also be at play. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.Publication Metadata only Performance study of a probabilistic multicast transport protocol(Elsevier Science Bv, 2004) N/A; Department of Computer Engineering; Özkasap, Öznur; Faculty Member; Department of Computer Engineering; College of Engineering; 113507Traditional reliable multicast protocols depend on assumptions about flow control and reliability mechanisms, and they suffer from a kind of interference between these mechanisms. This in turn affects the overall performance, throughput and scalability of group applications utilizing these protocols. However, there exists a substantial class of distributed applications for which the throughput stability and scalability guarantees are indispensable. Bimodal Multicast (Pbcast) is a new option in scalable reliable multicast protocols that uses an inverted protocol stack approach, in which probabilistic mechanisms are used at low layers, and reliability properties introduced closer to the application. The main contributions of this study are development of simulation models for performance evaluation of Bimodal Multicast, demonstration of how the inverted protocol stack approach works well on several network settings, and its comparison with best-effort reliable multicast mechanisms. Analysis results reveal that Bimodal Multicast, together with optimizations for improving its latency and reliability characteristics, scales well, exhibits stable throughput and in contrast to the other scalable reliable multicast mechanisms it gives predictable reliability even under highly perturbed conditions.