A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models by Jia‐Nan Jiang, Zi‐Ming Zou, Yang Lu, Jia Zhong, Yong Wang, Yu‐Zhang Ma, Bian‐Long Zhao

“…Through 5‐fold cross‐validation, it is determined that the average intersection over union, Dice coefficient, and pixel accuracy are all greater than 0.97. …”
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MoE-NuSeg: Enhancing nuclei segmentation in histology images with a two-stage Mixture of Experts network by Xuening Wu, Yiqing Shen, Qing Zhao, Yanlan Kang, Wenqiang Zhang

“…Evaluations across three datasets demonstrate that MoE-NuSeg outperforms the state-of-the-art methods, achieving an average increase of 0.99% in Dice coefficient, 1.14% in IoU and 0.92% in F1 Score, while reducing parameters by 30.1% and FLOPs by 40.2%. …”
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A 3D Dual Encoder Mirror Difference ResU-Net for Multimodal Brain Tumor Segmentation by Qiwei Xing, Zhihua Li, Yongxia Jing, Xiaolin Chen

“…When evaluated on the BraTS 2018 and BraTS 2019 datasets, our model achieves impressive Dice similarity coefficient (DSC) values of 0.862, 0.925, and 0.905 for Enhanced tumor (ET), Whole tumor (WT), and Tumor core (TC) in the former, and 0.869, 0.922, and 0.916 in the latter, respectively.…”
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Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence by Kilian Michiels, Eva Heffinck, Patricio Astudillo, Ivan Wong, Peter Mortier, Alessandra Maria Bavo

“…The predicted segmentation of the LA(A) was similar to the manual segmentation (dice score of 0.94 ± 0.02). The difference between the automatically predicted and manually measured perimeter-based diameter was −0.8 ± 1.3 mm (anatomical ostium), −1.0 ± 1.5 mm (Amulet landing zone), and −0.1 ± 1.3 mm (Watchman FLX landing zone), which is similar to the operator variability on these measurements. …”
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Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation by Haoran Wang, Gengshen Wu, Yi Liu

“…For instance, in evaluations on the CHAOS T2SPIR dataset, EGAUNet achieves approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher performance on the Jaccard metric, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher on the Dice metric, and nearly <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3</mn><mo>%</mo></mrow></semantics></math></inline-formula> higher on the precision metric in comparison to advanced networks such as Swin-Unet and TransUnet.…”
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The Effects of Emotional Schema Therapy on Social Health and Attitude Towards Social Harms Among Female Students by Fatemeh Eskandari, Milad Abedi ghlich ghashlaghi

“…Subsequently, using a random dice roll participants were allocated at random to either the experimental (n=15) or the control group (n=15). …”
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CooccurrenceAffinity: An R package for computing a novel metric of affinity in co-occurrence data that corrects for pervasive errors in traditional indices. by Kumar P Mainali, Eric Slud

“…The package supplements its main output of the novel metric of association with the three most common traditional indices of association in co-occurrence data: Jaccard, Sørensen-Dice, and Simpson.…”
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Medical image segmentation based on frequency domain decomposition SVD linear attention by Liu Qiong, Li Chaofan, Teng Jinnan, Chen Liping, Song Jianxiang

“…We demonstrated the segmentation validity and superiority of our model on the Abdominal Multi-Organ Segmentation dataset and the Dermatological Disease dataset, and on the Synapse dataset our model achieved a score of 82.68 on the Dice metrics and 17.23 mm on the HD metrics. Experimental results indicate that our model consistently exhibits segmentation effectiveness and improved accuracy across various datasets.…”
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RenalSegNet: automated segmentation of renal tumor, veins, and arteries in contrast-enhanced CT scans by Rashid Khan, Chao Chen, Asim Zaman, Jiayi Wu, Haixing Mai, Liyilei Su, Yan Kang, Bingding Huang

“…Evaluated on the KiPA dataset, RenalSegNet achieved remarkable performance, with an average dice score of 86.25%, IOU of 76.75%, Recall of 86.69%, Precision of 86.48%, HD of 15.78 mm, and AVD of 0.79 mm. …”
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Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System by Galib Muhammad Shahriar Himel, Md. Masudul Islam, Kh. Abdullah Al-Aff, Shams Ibne Karim, Md. Kabir Uddin Sikder

“…Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. …”
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LA LECTURA EN EL CONTEXTO SOCIAL FRONTERIZO EN LOS ESTUDIANTES DE EDU- CACIÓN BÁSICA: UNA VISIÓN CRÍTICA DESDE LA ZONA DE DESARROLLO PRÓXIMO DE LEV VYGOTSKY by Carlos Enrique Villamizar

“…La investigación se enfocó en el objetivo general que dice: analizar la incidencia de la lectura en el contexto social fronterizo en los estudiantes de educación básica, una visión crítica y constructiva a partir de la zona de desarrollo próximo de Lev Vygotsky, desarrollada en los estudiantes de grado quinto de educación básica primaria de la sede educativa N.º 3 Escuela Urbana Integrada Puerto Santander – Norte de Santander – Colombia. …”
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Ensemble Learning for Three-dimensional Medical Image Segmentation of Organ at Risk in Brachytherapy Using Double U-Net, Bi-directional ConvLSTM U-Net, and Transformer Network by Soniya Pal, Raj Pal Singh, Anuj Kumar

“…., TN + BCUN, the average Dice similarity coefficient (DSC) ranged from 0.992 to 0.998, and for DUN and BCUN (DUN + BCUN) combination, the average DSC ranged from 0.990 to 0.993, which reflecting high segmentation accuracy. …”
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MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions by Zili Chen, Yilong Peng, Jiadong Jiao, Aiguo Wang, Laigang Wang, Wei Lin, Yan Guo

“…The results demonstrated that MD-Unet achieved 92.75%, 90.94%, 84.93%, and 91.81% for the lesion CPA, recall, IoU, and F1 metrics, respectively, with an overall Dice score of 94.67%. Furthermore, the model parameters, floating-point operations, and inference time per single image for MD-Unet were 4.65 × 107, 2.3392 × 1011, and 65.096 ms, respectively. …”
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Molecular Typing of Klebsiella pneumoniae Clinical Isolates by Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction by Parinaz Sedighi, Omid Zarei, Kiana Karimi, Mohammad Taheri, Pezhman Karami, Leili Shokoohizadeh

“…ERIC profiles were compared using Dice method and clustered by UPGMA (unweighted pair group method with arithmetic mean) program. …”
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Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery by Hao Chen, Longfei Gou, Zhiwen Fang, Qi Dou, Haobin Chen, Chang Chen, Yuqing Qiu, Jinglin Zhang, Chenglin Ning, Yanfeng Hu, Haijun Deng, Jiang Yu, Guoxin Li

“…The AiLES was developed based on a dataset consisting of 5111 frames from 100 videos, using 4130 frames for model development and 981 frames for evaluation. The AiLES achieved a Dice score of 0.76 and a recognition speed of 11 frames per second, demonstrating robust performance in different metastatic extents (0.74–0.76) and locations (0.63–0.90). …”
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Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset by Xiaoying Pan, Yizhe Zhao, Hao Chen, De Wei, Chen Zhao, Zhi Wei

“…The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. …”
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SSMM-DS: A semantic segmentation model for mangroves based on Deeplabv3+ with swin transformer by Zhenhua Wang, Jinlong Yang, Chuansheng Dong, Xi Zhang, Congqin Yi, Jiuhu Sun

“…Finally, we optimized the loss function by combining cross-entropy loss and dice loss, addressing the issue of sampling imbalance caused by the small areas of mangroves. …”
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Advancements in Frank’s sign Identification using deep learning on 3D brain MRI by Sungman Jo, Jun Sung Kim, Min Jeong Kwon, Jieun Park, Jeong Lan Kim, Jin Hyeong Jhoo, Eosu Kim, Leonard Sunwoo, Jae Hyoung Kim, Ji Won Han, Ki Woong Kim

“…The optimal model was subsequently validated on two external datasets, comprising 300 brain MRI scans each with varying FS presence. Dice similarity coefficient (DSC) and receiver operating characteristic (ROC) analysis were employed to assess model performance. …”
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Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography by Bo Zhao, Jianjun Peng, Ce Chen, Yongyan Fan, Kai Zhang, Yang Zhang

“…Trained and evaluated on the CorArtTS2020 dataset, TransCHD achieved superior performance compared to state-of-the-art CNN- and transformer-based models, with a Dice score of 0.81 and an Intersection over Union (IoU) of 0.65. …”
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Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas by Begumhan BAYSAL, Mehmet Bilgin ESER, Mahmut Bilal DOGAN, Muhammet Arif KURSUN

“…Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). …”
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