標題: Titlebook: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di; Third MICCAI Worksho Shadi Albarqouni [打印本頁] 作者: Maculate 時間: 2025-3-21 20:09
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di影響因子(影響力)
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di影響因子(影響力)學科排名
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di網(wǎng)絡公開度
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di網(wǎng)絡公開度學科排名
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di被引頻次
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di被引頻次學科排名
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di年度引用
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di年度引用學科排名
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di讀者反饋
書目名稱Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di讀者反饋學科排名
作者: 輕彈 時間: 2025-3-21 21:57
https://doi.org/10.1007/978-1-349-00463-8d on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.作者: WATER 時間: 2025-3-22 01:08 作者: remission 時間: 2025-3-22 06:02
https://doi.org/10.1007/978-1-4684-6724-6ification and segmentation tasks. We observe 50–80% reduction in model size, 60–80% lesser number of parameters, 40–85% fewer FLOPs and 65–80% more energy efficiency during inference on CPUs. The code will be available at ..作者: 斗志 時間: 2025-3-22 09:10
A Specificity-Preserving Generative Model for?Federated MRI Translationd on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.作者: aspect 時間: 2025-3-22 16:05
Towards Real-World Federated Learning in?Medical Image Analysis Using Kaapanaframework used in RACOON to enable real-world federated learning in clinical environments. In addition, we create a benchmark of the nnU-Net when applied in multi-site settings by conducting intra- and cross-site experiments on a multi-site prostate segmentation dataset.作者: aspect 時間: 2025-3-22 18:43
Verifiable and?Energy Efficient Medical Image Analysis with?Quantised Self-attentive Deep Neural Netification and segmentation tasks. We observe 50–80% reduction in model size, 60–80% lesser number of parameters, 40–85% fewer FLOPs and 65–80% more energy efficiency during inference on CPUs. The code will be available at ..作者: 煞費苦心 時間: 2025-3-23 00:55
Conference proceedings 2022 Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event..DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusi作者: 冥想后 時間: 2025-3-23 01:25
Prototype Thermoballistic Model,obustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model’s size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.作者: 征稅 時間: 2025-3-23 06:52 作者: eulogize 時間: 2025-3-23 12:00 作者: 全部 時間: 2025-3-23 17:28 作者: 法律 時間: 2025-3-23 19:51 作者: OVER 時間: 2025-3-24 01:22 作者: Triglyceride 時間: 2025-3-24 03:46
978-3-031-18522-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Seminar 時間: 2025-3-24 07:26 作者: Coterminous 時間: 2025-3-24 13:14
Economies of Scale and Average Costtially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but 作者: Engaged 時間: 2025-3-24 16:04
The Social Practice of Informationng the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions. Data heterogeneity poses a challenge for FL and the personalization of the local作者: innate 時間: 2025-3-24 21:36 作者: 音樂戲劇 時間: 2025-3-24 23:54 作者: foreign 時間: 2025-3-25 07:12
The Thika Highway Improvement Project data partitioning, SL can be beneficial as it allows institutes with complementary features or images for a shared set of patients to jointly develop more robust and generalizable models. In this work, we propose “Split-U-Net" and successfully apply SL for collaborative biomedical image segmentatio作者: Bravura 時間: 2025-3-25 10:55 作者: 意見一致 時間: 2025-3-25 13:48 作者: finale 時間: 2025-3-25 19:47 作者: 曲解 時間: 2025-3-25 23:07 作者: 手銬 時間: 2025-3-26 00:08
William Atkinson and Richard Whytforde federated learning (FL) was proposed to build the predictive models, how to improve the security and robustness of a learning system to resist the accidental or malicious modification of data records are still the open questions. In this paper, we describe., a privacy-preserving decentralized medi作者: 脫離 時間: 2025-3-26 05:26
https://doi.org/10.1007/978-1-4684-6724-6g. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to esta作者: 障礙 時間: 2025-3-26 10:39
https://doi.org/10.1007/978-1-4684-6724-6pating institutions might not contribute equally - some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especiall作者: Adenoma 時間: 2025-3-26 14:24 作者: 繁忙 時間: 2025-3-26 18:38
https://doi.org/10.1007/978-1-4684-6724-6el sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs—by pruning the model parameters right before the communication step. More作者: 機警 時間: 2025-3-26 22:30 作者: CLASH 時間: 2025-3-27 04:26 作者: BALK 時間: 2025-3-27 07:01
https://doi.org/10.1007/978-1-4684-6724-6-of-the-art performance compared to classical image processing algorithms. However, the major downside of these methods is the high computational complexity, reliance on high-performance hardware like GPUs and the inherent black-box nature of the model. In this paper, we propose quantised stand-alon作者: flimsy 時間: 2025-3-27 12:54
Incremental Learning Meets Transfer Learning: Application to?Multi-site Prostate MRI Segmentationlinear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pretrained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to gener作者: 無法破譯 時間: 2025-3-27 15:51 作者: Invertebrate 時間: 2025-3-27 21:25
Data Stealing Attack on?Medical Images: Is It Safe to?Export Networks from?Data Lakes?ck then proceeds by exporting the compression decoder network together with some image codes that leads to the image reconstruction outside the data lake. We explore the feasibility of such attacks on databases of CT and MR images, showing that it is possible to obtain perceptually meaningful recons作者: 津貼 時間: 2025-3-28 01:31 作者: abstemious 時間: 2025-3-28 03:46 作者: 爆炸 時間: 2025-3-28 06:19 作者: 獎牌 時間: 2025-3-28 11:53 作者: 艱苦地移動 時間: 2025-3-28 15:03 作者: inhumane 時間: 2025-3-28 22:38
Towards More Efficient Data Valuation in?Healthcare Federated Learning Using Ensemblings values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each 作者: ABIDE 時間: 2025-3-28 23:11 作者: HERE 時間: 2025-3-29 06:39 作者: 動作謎 時間: 2025-3-29 08:43 作者: delta-waves 時間: 2025-3-29 14:08
Conference proceedings 2022 publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting..作者: 有機體 時間: 2025-3-29 17:57 作者: gerontocracy 時間: 2025-3-29 20:45 作者: fallible 時間: 2025-3-30 00:20
The Social Practice of Informationthe participants’ models. We compare the clustering approach with classical FedAvg and centralized training by evaluating our proposed methods on the HAM10k dataset for skin lesion classification with extreme heterogeneous data distribution. Our experiments demonstrate significant performance gain i作者: Instrumental 時間: 2025-3-30 08:07 作者: Halfhearted 時間: 2025-3-30 09:06 作者: 不容置疑 時間: 2025-3-30 15:27
https://doi.org/10.1007/978-1-349-00463-8ing the projected samples and generating synthetic images by interpolating the cluster centroids, thus reducing the possibility of collision with latent vectors corresponding to real samples and a consequent leak of sensitive information. The proposed approach is tested over two X-ray datasets for T作者: Malcontent 時間: 2025-3-30 20:33 作者: drusen 時間: 2025-3-31 00:17 作者: 柔美流暢 時間: 2025-3-31 04:12 作者: Self-Help-Group 時間: 2025-3-31 08:35 作者: Aerophagia 時間: 2025-3-31 12:26
https://doi.org/10.1007/978-1-4684-6724-6ing environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.作者: Volatile-Oils 時間: 2025-3-31 16:56
https://doi.org/10.1007/978-1-4684-6724-6r resolution, scanning rate, and imaging depth than a commercial OCT system. We use generative adversarial networks (GANs) to enhance the quality of this p-OCT data and then assess the impact of this enhancement on downstream performance of artificial intelligence (AI) algorithms for AMD detection. 作者: insular 時間: 2025-3-31 18:01 作者: 地殼 時間: 2025-3-31 22:48
Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource DiThird MICCAI Worksho作者: 直覺好 時間: 2025-4-1 02:25
