Performance and Efficiency Comparison of U-Net and Ghost U-Net in Road Crack Segmentation with Floating Point and Quantization Optimization by Haidhi Angkawijana Tedja, Onno W. Purbo

“…Results show that Ghost U-Net achieved a marginally higher performance, with an IoU of 0.5041 and a Dice coefficient of 0.6664, compared to U-Net’s IoU of 0.5034 and Dice coefficient of 0.6662. …”
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HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification by Junxiao Chen, Ruixue Wang, Wei Dong, Hua He, Shiyong Wang

“…The performance in nuclear segmentation was evaluated using the Dice Similarity Coefficient (DICE), the Aggregated Jaccard Index (AJI) and Panoptic Quality (PQ), and the classification performance was evaluated using F1 scores and category-specific F1 scores. …”
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Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks by A.V. Dobshik, S.K. Verbitskiy, I.A. Pestunov, K.M. Sherman, Yu.N. Sinyavskiy, A.A. Tulupov, V.B. Berikov

“…The suggested pipeline provides a Dice improvement of 12.0 %, sensitivity of 10.2 % and precision 10.0 % over the baseline and achieves an average Dice of 62.8 ± 3.3 %, sensitivity of 69.9 ± 3.9 %, specificity of 99.7 ± 0.2 % and precision of 61.9 ± 3.6 %, showing promising segmentation results.…”
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L’évaluation au cours de séances d’investigation en mathématiques by Michèle Gandit

“…This text presents the beginning of a research project (EvaCoDice) that tries to develop formative assessment in Inquiry-Based Science Education. …”
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Segment anything model for few-shot medical image segmentation with domain tuning by Weili Shi, Penglong Zhang, Yuqin Li, Zhengang Jiang

“…With just one labeled data, achieving a Dice score of 63.51%, a HD of 17.94 and an IoU score of 73.55% on Heart Task, on Prostate Task, an average Dice score of 46.01%, a HD of 10.25 and an IoU score of 65.92% were achieved, and the Dice, HD, and IoU score reaching 88.67%, 10.63, and 90.19% on BUSI. …”
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An In-depth Analysis of Rendered Models using Blender : A Research Result by Oluwafunmilayo Raheem, Bourdillon Omijeh, Chinagorom Ibeachu

“…To evaluate the efficiency of the virtual reality system we compared the web application with other existing web applications using parameters such as Accuracy, Precision, Jaccard index, Dice co-efficient, Processing time, and user rating. …”
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Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield by Nisha P. Shetty, Balachandra Muniyal, Ketavarapu Sriyans, Kunyalik Garg, Shiv Pratap, Aman Priyanshu, Dhruthi Kumar

“…The counterfactual reasoning framework of DICE outperforms LIME and DICE in offering finer insights into feature importance and the relative impact of different factors on yield prediction. …”
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SpaceCAM: A 16 nm FinFET Low-Power Soft-Error Tolerant TCAM Design for Space Communication Applications by Itay Merlin, Benjamin Zambrano, Marco Lanuzza, Alexander Fish, Avner Haran, Leonid Yavits

“…The Dual Interlocked Storage Cell (DICE) based memory is capable of withstanding soft errors. …”
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PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound by Jianguo Ju, Qian Zhang, Pengfei Xu, Tiange Liu, Cheng Li, Ziyu Guan

“…Extensive experiments on TRUS image datasets show that the PZS‐Net achieves higher accuracy in both the transitional zone (dice coefficient [Dice]: 68.90% ± 1.73%, mean intersection over union [mIoU]: 59.19% ± 2.09%, 95% Hausdorff distance [HD95]: 5.02 ± 0.83 mm) and the peripheral zone (Dice: 63.99% ± 3.16%, mIoU: 54.60% ± 3.35%, HD95: 5.28 ± 1.12 mm) and demonstrates the effectiveness and competitiveness of its key components via comprehensive ablation studies.…”
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State Treaty on Gambling: Finally regulated by Justus Haucap

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Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Maps Using Deep Learning Networks by Lida Zare Lahijan, Saeed Meshgini, Reza Afrouzian

“…However, this detection approach is inaccurate (the dice similarity coefficient is around 68%). Accordingly, several machine learning-based techniques have recently been proposed to improve the segmentation accuracy of ischemic stroke lesions. …”
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Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study by Rongxue Shan, Chenhao Pei, Qianrui Fan, Junchuan Liu, Dawei Wang, Shifeng Yang, Ximing Wang

“…The segmentation accuracies were evaluated by Dice coefficient (Dice), accuracy, recall, precision, and F1-score. …”
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Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet by Zhenyu Zhao, Shucheng Tan, Qinghua Zhang, Hui Chen

“…Furthermore, a new hybrid loss function, adaptive focal and Dice loss (AFD loss), is introduced through the adaptive AdaLoss algorithm by combining focal loss and Dice loss, improving the model’s ability to handle unbalanced samples. …”
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Presegmenter Cascaded Framework for Mammogram Mass Segmentation by Urvi Oza, Bakul Gohel, Pankaj Kumar, Parita Oza

“…Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. …”
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Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet by Dhafer Alhajim, Karim Ansari-Asl, Gholamreza Akbarizadeh, Mehdi Naderi Soorki

“…The proposed model was evaluated using the publicly available Lung Nodule Analysis 2016 (LUNA16) dataset, achieving a Dice Similarity Coefficient of 97.86%, IoU of 96.40%, sensitivity of 96.54%, and precision of 98.84%. …”
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Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models by Shokofeh Anari, Soroush Sadeghi, Ghazaal Sheikhi, Ramin Ranjbarzadeh, Malika Bendechache

“…Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. …”
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Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. by Yiye Zhang, Yufang Huang, Anthony Rosen, Lynn G Jiang, Matthew McCarty, Arindam RoyChoudhury, Jin Ho Han, Adam Wright, Jessica S Ancker, Peter Ad Steel

“…The model, which combined DICE and LR, boosted predictive performance while providing well-defined features. …”
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Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease by Parisa Khateri, Tiana Koottungal, Damon Wong, Rupert W. Strauss, Lucas Janeschitz-Kriegl, Maximilian Pfau, Leopold Schmetterer, Hendrik P. N. Scholl

“…Our model significantly outperforms standard models, achieving an average Dice coefficient of $$99\%$$ for total retina and $$93\%$$ for retinal sublayers. …”
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Experimental Activity with a Rover for Underwater Inspection by Erika Ottaviano, Agnese Testa, Pierluigi Rea, Marco Saccucci, Assunta Pelliccio, Maurizio Ruggiu

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Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors by Nalan Karunanayake, Lin Lu, Hao Yang, Pengfei Geng, Oguz Akin, Helena Furberg, Lawrence H. Schwartz, Binsheng Zhao

“…Results: Kidney and kidney tumor segmentations were evaluated against manual annotations as the reference standard. The model achieved a Dice score of 0.97 ± 0.02 for kidney organ segmentation. …”
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