A weak edge estimation based multi-task neural network for OCT segmentation. by Fan Yang, Pu Chen, Shiqi Lin, Tianming Zhan, Xunning Hong, Yunjie Chen

“…Our method surpasses other cutting-edge segmentation networks on two widely accessible datasets, achieving Dice scores of 84.09% and 93.84% on the HCMS and Duke datasets.…”
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Improving Unplugged Computational Thinking Skills Through Integrated Problem-Based and Differentiated Learning in Indonesia by Dewi Oktaviani, Susilo Satanti

“…The improvement of computational thinking ability is caused by: data collection activities in the form of dice/coin simulations, abstraction activities of sample points and important information in the problem, activities to formulate solution steps (algorithms) and data analysis activities in the form of interpretation of results and calculations. …”
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Unbalanced protocol recognition method based on improved residual U-Net by Jisheng WU, Zheng HONG, Tiantian MA

“…An unbalanced protocol recognition method based on the improved Residual U-Net was proposed to solve the challenge of network security posed by the increasing network attacks with the continuous development of the Internet.In the captured network traffic, a small proportion is constituted by malicious traffic, typically utilizing minority protocols.However, existing protocol recognition methods struggle to accurately identify these minority protocols when the class distribution of the protocol data is imbalanced.To address this issue, an unbalanced protocol recognition method was proposed, which utilized the improved Residual U-Net, incorporating a novel activation function and the Squeeze-and-Excitation Networks (SE-Net) to enhance the feature extraction capability.The loss function employed in the proposed model was the weighted Dice loss function.In cases where the recognition accuracies of the minority protocols were low, the loss function value would be high.Consequently, the optimization direction of the model would be dominated by the minority protocols, resulting in improved recognition accuracies for them.During the protocol recognition process, the network flow was extracted from the network traffic and preprocessed to convert it into a one-dimensional matrix.Subsequently, the protocol recognition model extracted the features of the protocol data, and the Softmax classifier predicted the protocol types.Experimental results demonstrate that the proposed protocol recognition model achieves more accurate recognition of the minority protocols compared to the comparison model, while also improving the recognition accuracies of the majority protocols.…”
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Freddy Taberna presente, ahora y siempre by Jinny Arancibia

“…A 50 años de su desaparición, su imagen es también una pérdida que, como dice Annie Erneaux, no tiene consuelo y “todo desemboca en esa ausencia que es el recuerdo”…”
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Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks by Jie Dong, Yueying Zhang, Yun Meng, Tingxiao Yang, Wei Ma, Huixin Wu

“…Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. …”
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Las mujeres en un país plurinacional e intercultural, Ecuador by Juan Illicachi Guzñay

“…El texto pretende ampliar el debate sobre el tema y se desea que las páginas que siguen sean leídas no únicamente como ejercicio de conocimiento sino también del reconocimiento y cuestionamiento a la distancia entre lo que se dice y lo que se hace. Los datos de este estudio se recogieron de forma colaborativa por el autor y un grupo de estudiantes de la Escuela de Psicología de la UNACH.…”
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River Segmentation of Remote Sensing Images Based on Composite Attention Network by Zhiyong Fan, Jianmin Hou, Qiang Zang, Yunjie Chen, Fei Yan

“…Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. …”
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Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc by Md. Abu Sayed, G. M. Mahmudur Rahman, Md. Sherajul Islam, Md. Alimul Islam, Jeongwon Park, Hasan Ahmed, Akram Hossain, Rahat Shahrior

“…Specifically, the classification accuracy of the WAE classification model reaches 97.64%, while the segmentation model achieves a Dice value of 95.72%. This automatic technique would improve the diagnostic process by offering enhanced accuracy and efficiency in the assessment of PLID.…”
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Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. by Solange Amorim Nogueira, Fernanda Ambrogi B Luz, Thiago Fellipe O Camargo, Julio Cesar S Oliveira, Guilherme Carvalho Campos Neto, Felipe Brazao F Carvalhaes, Marcio Rodrigues C Reis, Paulo Victor Santos, Giovanna Souza Mendes, Rafael Maffei Loureiro, Daniel Tornieri, Viviane M Gomes Pacheco, Antonio Paulo Coimbra, Wesley Pacheco Calixto

“…The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. …”
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Deep learning driven silicon wafer defect segmentation and classification by Rohan Ingle, Aniket K. Shahade, Mayur Gaikwad, Shruti Patil

“…The segmentation model mentioned in this study achieved a Mean Absolute Error (MAE) of 0.0036, a Root Mean Squared Error (RMSE) of 0.0576, a Dice Index (DSC) of 0.7731, and an Intersection over Union (IoU) of 0.6590. …”
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Kidney Segmentation with LinkNetB7 by Cihan Akyel

“…In this study, the LinkNetB7 model, which has not been previously used in renal segmentation studies, was modified and used. The study achieved a dice coefficient of 97.20%, precision of 97.30%, sensitivity of 97%, and recall of 97%. …”
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Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching by Gurman Gill, Reinhard R. Beichel

“…The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). …”
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USO DE RECURSOS TECNOLÓGICOS PARA EL DESARROLLO DEL PENSAMIENTO LÓGICO by Luis Alfonso Romero

“…Los datos fueron analizados mediante la estadística descriptiva, seguidamente se analizaron los resultados de manera cuantitativa como lo dice la metodología a través de tablas de frecuencia y gráficas, donde se apreció la importancia del uso de los recursos tecnológicos en el grado quinto para poder desarrollar el pensamiento lógico de los estudiantes. …”
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EL DESARROLLO ESTRATÉGICO DEL TRIBUNAL SUPREMO DE ISRAEL EN RELACIÓN CON LA OCUPACIÓN MILITAR by Yaniza Giraldo Restrepo

“…Entonces, se analiza el papel que tiene éste en su actividad creativa, que no se limita solo al análisis de lo que dice la ley sino a las posibles soluciones que pueden derivarse de la misma.…”
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Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support by John Sullivan, Apichon Witayangkurn

“…Employing a modified U-Net architecture with pre- and post-processing techniques, our experiments achieved an Intersection over Union score of 0.7879 and a Dice score of 0.8808. Image tiling and mosaicking with georeferencing were used to support large-scale imagery. …”
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Rapid Reconstruction of Acoustic Holograms Using Improved IU-Net With Adaptive Learning Rate Scheduling by Mingzhe Cui, Yang Li, Xuewei Wang, Jia Wang, Haoxuan Rong

“…Experimental results show that the proposed method achieves an accuracy of 90.9% in acoustic hologram reconstruction, with a Dice coefficient of 0.928, improving reconstruction speed by over 30% compared to traditional methods, ensuring high-quality and rapid reconstruction of acoustic holograms.…”
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Brain tumor segmentation by deep learning transfer methods using MRI images by E.Y. Shchetinin

“…The computational experimental analysis performed in the paper on a set of MRI brain images showed that the best encoder model for the above deep models was the DenseNet121 model with the following values of segmentation metrics Mean IoU of 91.34%, Mean Dice of 94.26%, Accuracy of 94.22%. The paper also comparatively analyses the results of the proposed segmentation method with several works of other authors. …”
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A feature-based approach for atlas selection in automatic pelvic segmentation. by Guoping Shan, Xue Bai, Yun Ge, Binbing Wang

“…A dataset of anonymized female pelvic Computed Tomography (CT) images demonstrated that MAS-SAGA significantly outperforms conventional multi-atlas-based segmentation (cMAS) in terms of Dice Similarity Coefficient (DSC) and 95th Percentile Hausdorff Distance (95HD) for bladder and rectum segmentation using a three-fold cross-validation strategy. …”
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Amar de mundo, amar de muerte: sobre el amor inútil en George Bataille by Javier Agüero Águila

“…En esta línea, el artículo se organiza en tres partes centrales. La primera de ellas dice relación con la plenitud metafísica que emerge en la fusión de los cuerpos en el instante del erotismo, y en la potencial trascendencia que desde ahí se anuncia. …”
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Automatic etiological classification of stroke thrombus digital photographs using a deep learning model by Álvaro Lucero-Garófano, Álvaro Lucero-Garófano, Alicia Aliena-Valero, Isabel Vielba-Gómez, Isabel Vielba-Gómez, Irene Escudero-Martínez, Irene Escudero-Martínez, Lluís Morales-Caba, Lluís Morales-Caba, Fernando Aparici-Robles, Fernando Aparici-Robles, Diana L. Tarruella Hernández, Diana L. Tarruella Hernández, Gerardo Fortea, Gerardo Fortea, José I. Tembl, José I. Tembl, Juan B. Salom, Juan B. Salom, José V. Manjón

“…The segmentation network achieved an average DICE coefficient of 0.96 [SD, 0.13]. The optimal fused imaging and clinical classification network had a 0.968 accuracy [95% CI, 0.935–0.994], and AUC of 0.947 [95% CI, 0.870–1]. …”
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