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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MODELOS DE ENSEÑANZA ENFOCADOS EN LAS TECNOLOGÍAS DIGITALES PARA LAS INSTITUCIONES DE EDUCACIÓN BÁSICA PRIMARIA DE CUCUTA NORTE DE SANTANDER - COLOMBIA by Sandra Esperanza Rojas Santiago

“… La educación en la actualidad ha permitido su vinculación con las tecnologías digitales, es así que emergen nuevos posicionamientos teóricos y prácticos que los docentes tienen a su alcance con la implementación de las tecnologías para lograr llevar el conocimiento de una manera efectiva, tal es el caso que desde la pandemia del COVID-19 el uso de las tecnologías se ha acentuado y se ha observado cómo los docentes hacen uso de las plataformas tecnológicas con la finalidad de que el estudiante tenga varios recursos para que pueda estudiar y por ende mejorar su rendimiento escolar; en efecto surge el siguiente objetivo general que dice: analizar los modelos de enseñanza enfocados en las tecnologías digitales para las instituciones de educación básica primaria de la ciudad de Cúcuta – Norte de Santander – Colombia. …”
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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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SF-SAM-Adapter: SAM-based segmentation model integrates prior knowledge for gaze image reflection noise removal by Ting Lei, Jing Chen, Jixiang Chen

“…We achieved segmentation metrics of IoU (Intersection over Union) = 0.749 and Dice = 0.853 at a memory size of 13.9 MB, outperforming recent techniques, including UNet, UNet++, BATFormer, FANet, MSA, and SAM2-Adapter. …”
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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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Pig tongue soft robot mimicking intrinsic tongue muscle structure by Yuta Ishikawa, Hiroyuki Nabae, Megu Gunji, Gen Endo, Koichi Suzumori

“…Additionally, we used the diffusible iodine-based contrast-enhanced computed tomography (Dice-CT) technique to observe the three-dimensional flow of muscle pathways. …”
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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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AI-based visualization of loose connective tissue as a dissectable layer in gastrointestinal surgery by Yuta Kumazu, Nao Kobayashi, Seigo Senya, Yuya Negishi, Kazuya Kinoshita, Yudai Fukui, Kazuhito Mita, Tomohiko Osaragi, Toshihiro Misumi, Hisashi Shinohara

“…Test images and videos were randomly sampled and model performance was evaluated visually by 10 external gastrointestinal surgeons. The mean Dice coefficient of the 50 images was 0.46. The AI model could detect at least 75% of the loose connective tissue in 91.8% of the images (459/500 responses). …”
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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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Las transformaciones semióticas para analizar estrategias de solución de problemas de interpretación y representación by Jesús David Berrío Valbuena, Sonia Valbuena Duarte, Rafael Sánchez Anillo

“…Asimismo, para poner de manifiesto qué hace y dice cada estudiante cuando responde las preguntas, se realizan videograbaciones y transcripciones. …”
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RHLS: A Robust Hybrid Level Set Model Using Global-Local Signed Energy-Based Pressure Force for Medical Image Segmentation by M. Almasganj, E. Fatemizadeh

“…It achieves an average accuracy of 97.2% and 95.6% on synthetic and real medical data, respectively, using the Dice similarity measure. These results validate the model&#x2019;s ability to handle various noise types and intensity variations, showcasing its potential for improved medical image segmentation tasks.…”
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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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Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing by Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, Adam Laing, Amber Shen, Sebastian Gomez, Sudip Mondal, Adela Ben-Yakar

“…To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments C. elegans in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36 GB data per device, phenotyping multiple body parameters within 35 min on a desktop PC. …”
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