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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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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Segmentasi Citra X-Ray Dada Menggunakan Metode Modifikasi Deeplabv3+ by Rima Tri Wahyuningrum, Maughfirotul Jannah, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

“…Dari 4 skenario tersebut skenario learning rate 0,01 dan menggunakan CLAHE mendapatkan hasil evaluasi tertinggi dengan menggunakan Dice Similarity Coefficient (DSC) sebesar 96,91%.   …”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…The results show that the suggested model performs substantially better than existing methods, achieving an amazing accuracy rate of 75%, recall rate of 83%, F1 score of 98%, Dice score of 98%, and precision of 73%, all while operating effectively in a time frame of 250 seconds.…”
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Retrospective evaluation of a novel ultrasound-based imaging analysis software for predicting radiofrequency ablation areas. by Masaya Sato, Ryosuke Tateishi, Yogev Zohar, Jiro Sato, Takeyuki Watadani, Makoto Moriyama, Taijiro Wake, Ryo Nakagomi, Mizuki Nishibatake Kinoshita, Takuma Nakatsuka, Tatsuya Minami, Koji Uchino, Kenichiro Enooku, Hayato Nakagawa, Yoshinari Asaoka, Ryo Yamada, Nitzan Even, Inbal Amitai, Yossi Abu, Mitsuhiro Fujishiro, Kazuhiko Koike

“…<h4>Results</h4>The median tumor measurement along the major axis was 1.2 cm. The median Dice coefficient, Sensitivity, and Precision between BioTrace and CECT were 0.90, 0.91, and 0.91, respectively. …”
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Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy by Josh Mason, Jack Doherty, Sarah Robinson, Meagan de la Bastide, Jack Miskell, Ruth McLauchlan

“…For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. …”
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Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection by Jae Won Seo, Young Jae Kim, Kwang Gi Kim

“…Using VGGnet16, PMVnet achieved a Dice Similarity Coefficient (DSC) of 0.709 in segmentation and a recall of 0.950 at 0.156 false positives per image (FPPI) in detection tasks, outperforming the single-view model, which had a DSC of 0.579 and a recall of 0.813 at 0.188 FPPI. …”
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Llegar al lugar correcto by Fernando Bárcena

“…El maestro transmite, sobre todo, lo que no dice. Su comunicación es indirecta, y a menudo parece que en la transmisión del saber hay una clase de engaño que adopta la forma de la ironía, un juego de ocultamiento y exhibición. …”
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EMFF-Net: Edge-Enhancement Multi-Scale Feature Fusion Network by Xuhui Guan, Jiwang Zhou, Jian Chen, Xiaodan Xu, Yizhang Jiang, Kaijian Xia

“…This represents a clear advantage over traditional CNNs and existing SOTA techniques, Especially, we achieved 81% mDice and 74% mIoU on the CVC-ColonDB dataset.…”
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Decision Making under Risk Condition in Patients with Parkinson’s Disease: A Behavioural and fMRI Study by Kirsten Labudda, Matthias Brand, Markus Mertens, Isabelle Ollech, Hans J. Markowitsch, Friedrich G. Woermann

“…We investigated ten cognitively intact PD patients and twelve healthy subjects with the Game of Dice Task (GDT) to assess risky decision making, and with an fMRI paradigm to analyse the neural correlates of information integration in the deliberative decision phase. …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. …”
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Interculturalidad, plurinacionalidad y decolonialidad: las insurgencias político-epistémicas de refundar el Estado by CATHERINE WALSH

“…Requiere pasar de las resistencias a nuevas insurgencias -de transgredir, interrumpir, incidir e in-surgir-; al poner como meollo del asunto, los patrones del poder colonial que aún perviven para -y desde allí- plantear, cultivar y ejercitar articulaciones y construcciones distintas que alienten un cambio radical y descolonizador que pretende no solo acabar con el Estado colonial y el modelo neoliberal -como dice Evo Morales-, sino también hacer entre todos una patria distinta.…”
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Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images by Soichiro Inomata, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori

“…The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared. …”
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Las «otras mujeres» y la Pedagogía de la Autonomía de Freire by Mireia Arrufat Gallardo

“…Así, mujeres de muy diferentes culturas y estilos de vida deciden qué educación quieren, cómo la quieren, para qué, qué sueños tienen… Somos estando, dice Freire, desde la coherencia, la ilusión, la solidaridad…”
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Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network by Ankit Kandpal, Rakesh Kumar Gupta, Anup Singh

“…The network achieved a dice score of 0.65 ± 0.19 and 0.45 ± 0.32 on the training and testing datasets. …”
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An attention based residual U-Net with swin transformer for brain MRI segmentation by Tazkia Mim Angona, M. Rubaiyat Hossain Mondal

“…It achieved an average Dice Similarity Coefficients (DSC) of 88.27 % and average Intersection over Union (IoU) of 79.93 % on BraTS 2020. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…Evaluation metrics such as Accuracy, Dice score, and Sensitivity are commonly employed, with some models demonstrating robustness across multi-center datasets. …”
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Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net by Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan

“…The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models. …”
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