In this paper, we study the influence of additives in the filling process of via holes for LSI Cu interconnections by using the kinetic Monte Carlo method. As a model for electroplating, we extended the Solid‐by‐Sol...
In this paper, we study the influence of additives in the filling process of via holes for LSI Cu interconnections by using the kinetic Monte Carlo method. As a model for electroplating, we extended the Solid‐by‐Solid model for crystal growth to include additives which inhibit the adsorption of new atoms. This enables us to control the local surface growth rate to find out the optimal deposition condition for void‐free filling. The distribution of additives on the surface and their influence on the surface and void structures are carefully examined.
Summary: Background : CT colonography was found to be sensitive and specific for detection of colonic polyps and colorectal cancer (CRC). Depending on the software used, CT colonography requires a certain amount of op...
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Summary: Background : CT colonography was found to be sensitive and specific for detection of colonic polyps and colorectal cancer (CRC). Depending on the software used, CT colonography requires a certain amount of operator interaction, which limits it's widespread usage. The goal of this papers is to present two novel automated techniques for displaying CT colonography: virtual dissection and automated colonic polyp detection. Methods : Virtual dissection refers to a technique where the entire colon is virtually stretched and flattened thus simulating the view on the pathologist's table.
Colonic folds show a ‘global outward bulging of the contour’, whereas colonic polyps exhibit the inverse (‘local inward bulging’). This feature is used to map areas of ‘local inward bulging’ with colours on 3D reconstructions. A cadaveric phantom with 13 artificially inserted polyps was used for validation of both techniques. Results : On virtual dissection all 13 inserted polyps could be identified. They appeared either as bumps or as local broadening of colonic folds. In addition, the automated colonic polyp detection algorithm was able to tag all polyps. Only 10 min of operator interaction were necessary for both techniques. Conclusions : Virtual dissection overcomes the shortcomings of CT colonography, and automated colonic polyp detection establishes a roadmap of the polyps. Zusammenfassung: Grundlagen : Die CT-Kolographie ermöglicht die Detektion von Kolonpolypen und kolorektalen Karzinomen. In Abhängigkeit von der verwendeten Software ist ein unterschiedlich großer Zeitaufwand notwendig, der die breite klinische Anwendung limitiert. Zweck der vorliegenden Arbeit ist die Präsentation zweier neuer Darstellungsverfahren für die CT-Kolographie: virtuelle Dissektion und automatische Detektion von Dickdarmpolypen. Methodik : Die virtuelle Dissektion ist eine Technik, bei der das gesamte Kolon aus den Spiral-CT-Daten extrahiert, artifiziell gestreckt, in seiner Längsrichtung aufge
The surface structure and vacancy formation during the growth of thin film have been studied with use of a kinetic Monte Carlo method. By extending Solid-on-Solid model to a model which allows vacancies to form in the...
The surface structure and vacancy formation during the growth of thin film have been studied with use of a kinetic Monte Carlo method. By extending Solid-on-Solid model to a model which allows vacancies to form in the film, we investigated both the surface structure and the void structure, and interrelation between them. It is shown that point defects appear in the film when the surface has a layer structure, while large voids consisting of more than one vacant site are formed when the surface becomes rough. The spatial distribution of vacancies is closely related to the surface structure.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
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