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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

State Treaty on Gambling: Finally regulated by Justus Haucap

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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%. …”
Get full text

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. …”
Get full text

Experimental Activity with a Rover for Underwater Inspection by Erika Ottaviano, Agnese Testa, Pierluigi Rea, Marco Saccucci, Assunta Pelliccio, Maurizio Ruggiu

Get full text

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. …”
Get full text

Promote Renewable Energies Efficiently and Effectively by Arbeitsgruppe „Energiesysteme der Zukunft“, Justus Haucap, Jürgen Kühling, Munib Amin, Gert Brunekreeft, Dörte Fouquet, Veronika Grimm, Jörg Gundel, Martin Kment, Wolfgang Ketter, Jochen Kreusel, Charlotte Kreuter-Kirchhof, Mario Liebensteiner, Albert Moser, Marion Ott, Christian Rehtanz, Heike Wetzel, Jonathan Meinhof, Marlene Wagner, Miriam Borgmann, Cyril Stephanos

Get full text

Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy by Alexandra Walter, Cornelius J. Bauer, Ama Katseena Yawson, Philipp Hoegen-Saßmannshausen, Sebastian Adeberg, Jürgen Debus, Oliver Jäkel, Martin Frank, Kristina Giske

“…Both sets of segmentations are evaluated using DICE, Hausdorff Distance and surface DICE. We investigate their impact on the build-up of the biomechanical articulated skeleton model by deviations in joint positioning and CT-CT registration accuracy using target registration error (TRE). …”
Get full text

Machine Learning-Based Normal White Blood Cell Multi-Classification Optimization by Taeyeon Gil, Sukjun Lee, Onseok Lee

“…The nucleus showed high segmentation performance with an average accuracy of 98.58% and a Dice coefficient of 0.9233, whereas the cells achieved an average accuracy of 99.47% and a Dice coefficient of 0.9324. …”
Get full text

An Attention-Based Residual U-Net for Tumour Segmentation Using Multi-Modal MRI Brain Images by Najme Zehra Naqvi, K. R. Seeja

“…For BraTS 2020 it achieved Dice Coefficient of 0.9978, 0.9378 and 0.9478 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively and for BraTS 2018 it achieved Dice Coefficient 98.32, 93.32 and 92.32 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively.…”
Get full text

Alasdair MacIntyre: relatividad conceptual, tomismo y liberalismo by Carlos Isler S.

“…Se expone esta teoría y se analiza su compatibilidad con el tomismo, tradición a la que MacIntyre dice pertenecer, y con el liberalismo, tradición a la que critica con vigor.…”
Get full text

LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation by Guanghong Deng, Bing Yu, Wenlong Jing, Yong Li, Xiaodan Zhao

“…The experimental IoU value of the International Skin Imaging Collaboration (ISIC 2018) data set reached 82.95%, and the Dice value reached 90.50%. The single inference speed reached 842.58[Formula: see text]ms. …”
Get full text

A Hybrid Efficient U-Net Framework for Detection of Anterior Belly of the Digastric Muscle on Ultrasonography by Sule Erdem, Suheda Erdem, Muammer Turkoglu, Abdulkadir Sengur, Nebras M. Sobahi

“…Combo Loss (a combination of Binary Cross-Entropy and Dice Loss) was used to train the model and segmentation metrics such as F1-score, Intersection over Union (IoU) and Dice Co-efficient were used to evaluate performance. …”
Get full text

Lack of Data and other Problems of Economic Policy Advice in Germany by Justus Haucap

Get full text