標題: Titlebook: Computational Mathematics Modeling in Cancer Analysis; Second International Wenjian Qin,Nazar Zaki,Chao Li Conference proceedings 2023 The [打印本頁] 作者: Maculate 時間: 2025-3-21 18:47
書目名稱Computational Mathematics Modeling in Cancer Analysis影響因子(影響力)
書目名稱Computational Mathematics Modeling in Cancer Analysis影響因子(影響力)學科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis網(wǎng)絡公開度
書目名稱Computational Mathematics Modeling in Cancer Analysis網(wǎng)絡公開度學科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis被引頻次
書目名稱Computational Mathematics Modeling in Cancer Analysis被引頻次學科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis年度引用
書目名稱Computational Mathematics Modeling in Cancer Analysis年度引用學科排名
書目名稱Computational Mathematics Modeling in Cancer Analysis讀者反饋
書目名稱Computational Mathematics Modeling in Cancer Analysis讀者反饋學科排名
作者: 天然熱噴泉 時間: 2025-3-21 23:12 作者: AMPLE 時間: 2025-3-22 02:35
Peter M. Winter,Leonard L. Firestone lowering the burden of HR image annotation is a practical and cost-effective topic to save more human and material resources in the dataset preparation. In this work, we proposed a label-efficient cross-resolution polyp segmentation framework via unsupervised domain adaption with unlabeled HR image作者: headlong 時間: 2025-3-22 05:37
Peter M. Winter,Leonard L. Firestoneve been successful in automating this process, the reliance on local textures can negatively impact model performance in the presence of pathological conditions such as brain tumors. This study presents a novel yet practical approach to offer supplementary texture-invariant spatial information of th作者: SYN 時間: 2025-3-22 12:12
Peter M. Winter,Leonard L. Firestonecal cancer. . We retrospectively included 98 patients with cervical cancer (54 well/moderately differentiated and 44 poorly differentiated). Radiomics features were extracted from T2WI Axi and T2WI Sag. Feature selection was performed by intra-class correlation coefficients (ICC), t-test, least abso作者: echnic 時間: 2025-3-22 12:53 作者: echnic 時間: 2025-3-22 20:26
https://doi.org/10.1007/978-1-4899-2657-9ric, single-snapshot magnetic resonance imaging (mpMRI) scan. We model the dynamics of proliferative, infiltrative, and necrotic tumor cells and their coupling to oxygen concentration. Fitting the PDE to the data is a formidable inverse problem as we need an estimate of the healthy subject anatomy, 作者: 苦笑 時間: 2025-3-22 22:28 作者: 防止 時間: 2025-3-23 01:29 作者: Mediocre 時間: 2025-3-23 05:58 作者: irreducible 時間: 2025-3-23 11:59 作者: corporate 時間: 2025-3-23 17:17 作者: Critical 時間: 2025-3-23 22:06 作者: Factorable 時間: 2025-3-24 01:13 作者: miscreant 時間: 2025-3-24 05:09
https://doi.org/10.1007/BFb0030514for early screening of hepatocellular carcinoma (HCC). However, the complex morphology and wide variations of liver and tumors in MRI images may not be fully captured by relying solely on pixel-level information. Therefore, combining shape-aware information becomes critical, as it provides additiona作者: 帶子 時間: 2025-3-24 09:25 作者: 聯(lián)邦 時間: 2025-3-24 12:42
https://doi.org/10.1007/978-1-940033-37-2ditionally done manually by oncologists, a process that is time-consuming and subject to individual subjectivity. Although fully automated deep-learning models could offer a solution, their segmentation performance is often hindered by the lack of abundant annotated data. We retrospectively analyzed作者: Obstruction 時間: 2025-3-24 15:52
Studies of Vortex Dominated Flowsration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions whe作者: consolidate 時間: 2025-3-24 20:41
Conference proceedings 2023n October 8, 2023, in Vancouver, BC, Canada.??..The 17 full papers presented were carefully reviewed and selected from 25 submissions.?The conference focuses on?the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of mathematical cancer data analysis.. . . .作者: 勛章 時間: 2025-3-24 23:34
Computational Mathematics Modeling in Cancer Analysis978-3-031-45087-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 釋放 時間: 2025-3-25 04:04
https://doi.org/10.1007/978-3-031-45087-7Computer Science; Cancer imaging analysis; Computer-aided tumor detection; Multi-modality; Mathematics m作者: Ankylo- 時間: 2025-3-25 09:47
978-3-031-45086-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Tdd526 時間: 2025-3-25 13:39
Virtual Contrast-Enhanced MRI Synthesis with High Model Generalizability Using Trusted Federated Leevention was visually assessed by reviewing the excluded images after training. Three single institutional models (separately trained with single institutional data), a joint model (jointly trained using multi-institutional data), and two popular federated learning frameworks (FedAvg and FedProx) we作者: 牙齒 時間: 2025-3-25 17:39 作者: FLAX 時間: 2025-3-25 23:01
The Value of Ensemble Learning Model Based on Conventional Non-Contrast MRI in the Pathological Graaverage AUC was 0.74(0.69,0.76) and the accuracy was 0.73. It was followed by SVM, LR and KNN models, and the average AUC were 0.73(0.66,0.80), 0.71(0.62,0.78) and 0.66(0.61,0.72), respectively. The performance of stacking ensemble model showed effective improvement, with an average AUC of 0.77(0.67作者: 宴會 時間: 2025-3-26 03:32
,Federated Multi-organ Dynamic Attention Segmentation Network with?Small CT Dataset, clients and the unseen external testing dataset from the center server. The experimental results show that the proposed federated aggregation scheme improves the generalization ability of the model in a smaller training dataset and partially alleviates the problem of class imbalance.作者: 增強 時間: 2025-3-26 04:35 作者: ellagic-acid 時間: 2025-3-26 08:47
,Advancing Delineation of?Gross Tumor Volume Based on?Magnetic Resonance Imaging by?Performing Sourcansfers knowledge of tumor segmentation learned in the source domain to the unlabeled target dataset without the access to the source dataset and annotate the target domain, for the NPC. Specifically, We enhances model performance by jointly optimizing entropy minimization and pseudo-labeling based 作者: 不可思議 時間: 2025-3-26 16:26 作者: STRIA 時間: 2025-3-26 19:36
Fully Convolutional Transformer-Based GAN for Cross-Modality CT to PET Image Synthesis,lled C2P-GAN for cross-modality synthesis of PET images from CT images. It composed of a generator and a discriminator that compete with each other, as well as a registration network that can eliminate noise interference. The generator integrates convolutional networks that excel in capturing local 作者: Chipmunk 時間: 2025-3-26 23:11 作者: COMA 時間: 2025-3-27 02:27
,MPSurv: End-to-End Multi-model Pseudo-Label Model for?Brain Tumor Survival Prediction with?Populatiset for the training and validation of segmentation and prediction tasks. Experimental results demonstrate that our model enhances the accuracy of brain tumor survival prediction and exhibits superior generalizability. The source code is available at: ..作者: 拉開這車床 時間: 2025-3-27 06:09
Shape-Aware Diffusion Model for Tumor Segmentation on Gd-EOB-DTPA MRI Images of Hepatocellular Carcfor effectively adapting to the variable characteristics of liver and tumor geometries, boundary shapes to achieve more accurate segmentation of HCC on Gd-EOB-DTPA MRI images. We conducted validation experiments on Gd-EOB-DTPA MRI images from 25 HCC patients, and the results demonstrated Dice and Io作者: 獨裁政府 時間: 2025-3-27 12:06
,Style Enhanced Domain Adaptation Neural Network for?Cross-Modality Cervical Tumor Segmentation,to Domain Adversarial Neural Network (DANN)-based model to improve the generalization performance of the shared segmentation network. Experimental results show that our method achieves the best performance on the cross-modality cervical tumor segmentation task compared to current state-of-the-art UD作者: CURT 時間: 2025-3-27 16:49 作者: 紅腫 時間: 2025-3-27 21:30 作者: 常到 時間: 2025-3-28 00:26
Peter M. Winter,Leonard L. Firestoneevention was visually assessed by reviewing the excluded images after training. Three single institutional models (separately trained with single institutional data), a joint model (jointly trained using multi-institutional data), and two popular federated learning frameworks (FedAvg and FedProx) we作者: Hot-Flash 時間: 2025-3-28 03:53 作者: entice 時間: 2025-3-28 07:04
Peter M. Winter,Leonard L. Firestoneaverage AUC was 0.74(0.69,0.76) and the accuracy was 0.73. It was followed by SVM, LR and KNN models, and the average AUC were 0.73(0.66,0.80), 0.71(0.62,0.78) and 0.66(0.61,0.72), respectively. The performance of stacking ensemble model showed effective improvement, with an average AUC of 0.77(0.67作者: Astigmatism 時間: 2025-3-28 14:24 作者: UNT 時間: 2025-3-28 16:34 作者: 上下連貫 時間: 2025-3-28 20:38 作者: notice 時間: 2025-3-29 00:54
Three-Dimensional Velocity-Map Imaging,es. Finally, a Transformer integrated with subtype contrastive loss is proposed for effective aggregation and WSI-level prediction. Experimental results on the dataset from cooperative hospital demonstrate the effectiveness of our proposed framework. The BM-SMIL framework has the potential to enhanc作者: 驚呼 時間: 2025-3-29 05:07 作者: 自作多情 時間: 2025-3-29 08:08 作者: 豪華 時間: 2025-3-29 14:30
Lecture Notes in Computer Scienceset for the training and validation of segmentation and prediction tasks. Experimental results demonstrate that our model enhances the accuracy of brain tumor survival prediction and exhibits superior generalizability. The source code is available at: ..作者: –LOUS 時間: 2025-3-29 17:53
https://doi.org/10.1007/BFb0030514for effectively adapting to the variable characteristics of liver and tumor geometries, boundary shapes to achieve more accurate segmentation of HCC on Gd-EOB-DTPA MRI images. We conducted validation experiments on Gd-EOB-DTPA MRI images from 25 HCC patients, and the results demonstrated Dice and Io作者: Gentry 時間: 2025-3-29 22:17
Lecture Notes in Computer Scienceto Domain Adversarial Neural Network (DANN)-based model to improve the generalization performance of the shared segmentation network. Experimental results show that our method achieves the best performance on the cross-modality cervical tumor segmentation task compared to current state-of-the-art UD作者: 全能 時間: 2025-3-30 03:42 作者: Lipohypertrophy 時間: 2025-3-30 06:50 作者: 口味 時間: 2025-3-30 08:14
Computational Mathematics Modeling in Cancer AnalysisSecond International作者: 過于平凡 時間: 2025-3-30 13:34
Conference proceedings 2023n October 8, 2023, in Vancouver, BC, Canada.??..The 17 full papers presented were carefully reviewed and selected from 25 submissions.?The conference focuses on?the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of mathematica作者: Fluctuate 時間: 2025-3-30 19:52 作者: 敲詐 時間: 2025-3-31 00:43
Gauss: The Great Asteroid Treatises,prior knowledge of CAC signal. The experimental results show that the strategies proposed is simple and effective. On the four-color FISH image, when using only 8% of labeled data is used, it can all achieve 0.15% 0.41% 0.55% and 0.85% F1 score improvements compared to the supervised baseline.作者: GROVE 時間: 2025-3-31 02:12 作者: VERT 時間: 2025-3-31 08:29
Domain Knowledge Adapted Semi-supervised Learning with Mean-Teacher Strategy for Circulating Abnormprior knowledge of CAC signal. The experimental results show that the strategies proposed is simple and effective. On the four-color FISH image, when using only 8% of labeled data is used, it can all achieve 0.15% 0.41% 0.55% and 0.85% F1 score improvements compared to the supervised baseline.作者: 千篇一律 時間: 2025-3-31 13:08
0302-9743 AI 2023, on October 8, 2023, in Vancouver, BC, Canada.??..The 17 full papers presented were carefully reviewed and selected from 25 submissions.?The conference focuses on?the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of m作者: POINT 時間: 2025-3-31 15:28 作者: ascetic 時間: 2025-3-31 21:02 作者: stressors 時間: 2025-4-1 01:11
0302-9743 onference focuses on?the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of mathematical cancer data analysis.. . . .978-3-031-45086-0978-3-031-45087-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Intrepid 時間: 2025-4-1 02:06
Virtual Contrast-Enhanced MRI Synthesis with High Model Generalizability Using Trusted Federated Leesis. The FL-TrustVCE is featured with patient privacy preservation, data poisoning prevention, and multi-institutional data training. For FL-TrustVCE development, we retrospectively collected MRI data from 18 institutions, in total 438 patients were involved. For each patient, T1-weighted MRI, T2-w
