標題: Titlebook: Domain Adaptation and Representation Transfer; 5th MICCAI Workshop, Lisa Koch,M. Jorge Cardoso,Dong Yang Conference proceedings 2024 The Ed [打印本頁] 作者: GOLF 時間: 2025-3-21 16:27
書目名稱Domain Adaptation and Representation Transfer影響因子(影響力)
書目名稱Domain Adaptation and Representation Transfer影響因子(影響力)學科排名
書目名稱Domain Adaptation and Representation Transfer網(wǎng)絡公開度
書目名稱Domain Adaptation and Representation Transfer網(wǎng)絡公開度學科排名
書目名稱Domain Adaptation and Representation Transfer被引頻次
書目名稱Domain Adaptation and Representation Transfer被引頻次學科排名
書目名稱Domain Adaptation and Representation Transfer年度引用
書目名稱Domain Adaptation and Representation Transfer年度引用學科排名
書目名稱Domain Adaptation and Representation Transfer讀者反饋
書目名稱Domain Adaptation and Representation Transfer讀者反饋學科排名
作者: 主動脈 時間: 2025-3-22 00:16
https://doi.org/10.1007/978-3-031-13913-0itectures. Such approaches, however, typically ignore domain specific peculiarities and lack the ability to generalize outside their training dataset. We observe that, in RGB images, teeth display a weak or unremarkable texture while exhibiting strong boundaries; similarly, in panoramic radiographs 作者: Deadpan 時間: 2025-3-22 01:38 作者: dendrites 時間: 2025-3-22 06:08
Vladimir I. Trukhachev,Rafkat S. Gaisin to pre-existing labels. The method involves utilizing a self-training approach by generating pseudo-labels of the target domain data. To do so, a strategy that is based on a smooth transition between domains is implemented where we initially feed easy examples to the network and gradually increase 作者: 銀版照相 時間: 2025-3-22 08:49 作者: 墊子 時間: 2025-3-22 14:22 作者: 墊子 時間: 2025-3-22 17:45 作者: A簡潔的 時間: 2025-3-22 23:23 作者: 有組織 時間: 2025-3-23 02:35
Public Spaces in ‘Colonized’ Urban Iberiaassing various modalities, sequences, manufacturers and machines. In this study, we propose a semi-supervised domain adaptation (SSDA) framework for automatically detecting poor quality FLAIR MRIs within a clinical data warehouse. Leveraging a limited number of labelled FLAIR and a large number of l作者: 退潮 時間: 2025-3-23 08:55 作者: Inkling 時間: 2025-3-23 13:22
Unilateral Biportal Endoscopy of the Spinem large ones. As the number of DL architectures explodes, exhaustively attempting all candidates becomes unfeasible, motivating cheaper alternatives for choosing them. Transferability scoring methods emerge as an enticing solution, allowing to efficiently calculate a score that correlates with the a作者: fixed-joint 時間: 2025-3-23 15:03 作者: dominant 時間: 2025-3-23 18:55
Unilateral Biportal Endoscopy of the Spineecent Deep Learning solutions, which can hinder future adoption. Particularly, the vulnerability of Vision Transformer (ViT) to adversarial, privacy, and confidentiality attacks raise serious concerns about their reliability in medical settings. This work aims to enhance the robustness of self-ensem作者: 制定法律 時間: 2025-3-24 00:15 作者: 打折 時間: 2025-3-24 04:45 作者: Amendment 時間: 2025-3-24 09:52
Anke Schr?der,Melanie Schlüter,Maurice Illisuch model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior pr作者: Bravura 時間: 2025-3-24 10:58 作者: Jacket 時間: 2025-3-24 16:34
978-3-031-45856-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Maximizer 時間: 2025-3-24 19:38
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/282480.jpg作者: 走調(diào) 時間: 2025-3-25 00:53
Domain Adaptation and Representation Transfer978-3-031-45857-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: leniency 時間: 2025-3-25 06:28 作者: ONYM 時間: 2025-3-25 07:44 作者: IVORY 時間: 2025-3-25 13:16
,Black-Box Unsupervised Domain Adaptation for?Medical Image Segmentation,ng. In general, UDA assumes that information about the source model, such as its architecture and weights, and all samples from the source domains are available when a target domain model is trained. However, this is not a realistic assumption in applications where privacy and white-box attacks are 作者: promote 時間: 2025-3-25 19:03 作者: biopsy 時間: 2025-3-25 23:33 作者: 枯燥 時間: 2025-3-26 03:41 作者: 不近人情 時間: 2025-3-26 08:08
,Realistic Data Enrichment for?Robust Image Segmentation in?Histopathology,ng large-scale WSIs manually is a demanding and time-consuming task, unlikely to yield the expected results when used for fully supervised learning systems. Rarely observed disease patterns and large differences in object scales are difficult to model through conventional patient intake. Prior metho作者: 柱廊 時間: 2025-3-26 08:32 作者: Synthesize 時間: 2025-3-26 14:48
,Semi-supervised Domain Adaptation for?Automatic Quality Control of?FLAIR MRIs in?a?Clinical Data Waassing various modalities, sequences, manufacturers and machines. In this study, we propose a semi-supervised domain adaptation (SSDA) framework for automatically detecting poor quality FLAIR MRIs within a clinical data warehouse. Leveraging a limited number of labelled FLAIR and a large number of l作者: 大方一點 時間: 2025-3-26 17:29
,Towards Foundation Models Learned from?Anatomy in?Medical Imaging via?Self-supervision,s: (1) .: each anatomical structure is morphologically distinct from the others; and (2) .: each anatomical structure is an integrated part of a larger whole. We envision a foundation model for medical imaging that is . and . developed upon this foundation to gain the capability of “understanding” h作者: 截斷 時間: 2025-3-27 00:50 作者: 職業(yè) 時間: 2025-3-27 04:48
,DGM-DR: Domain Generalization with?Mutual Information Regularized Diabetic Retinopathy Classificati the performance of models trained with the independent and identically distributed (i.i.d) assumption deteriorates when deployed in the real world. This problem is exacerbated in the medical imaging context due to variations in data acquisition across clinical centers, medical apparatus, and patien作者: 發(fā)芽 時間: 2025-3-27 06:12
,SEDA: Self-ensembling ViT with?Defensive Distillation and?Adversarial Training for?Robust Chest X-Recent Deep Learning solutions, which can hinder future adoption. Particularly, the vulnerability of Vision Transformer (ViT) to adversarial, privacy, and confidentiality attacks raise serious concerns about their reliability in medical settings. This work aims to enhance the robustness of self-ensem作者: 別名 時間: 2025-3-27 12:44
A Continual Learning Approach for Cross-Domain White Blood Cell Classification,al settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. H作者: FECT 時間: 2025-3-27 14:11
Metadata Improves Segmentation Through Multitasking Elicitation,had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorporate metadata by employing a channel modulation mechanism in convolutional networks and study its effect on semantic segmentation tasks. We demonstrate that metadata as additional input to a convolutio作者: 真實的你 時間: 2025-3-27 19:56 作者: 刪除 時間: 2025-3-27 22:10
https://doi.org/10.1007/978-3-031-13173-8s. Instead of regarding the entire dataset as a source or target domain, the dataset is processed based on the dominant factor of data variations, which is the scanner manufacturer. Afterwards, the target domain’s feature space is aligned pairwise with respect to each source domain’s feature map. Ex作者: Yourself 時間: 2025-3-28 02:52
https://doi.org/10.1007/978-3-031-13913-0obust features, we can achieve better segmentation and detection results. Additionally, MultiVT improves generalization capabilities without applying domain adaptive techniques - a characteristic which renders our method suitable for use in real-world applications.作者: prostatitis 時間: 2025-3-28 08:22
Svetlana G. Cheglakova,Тatyana А. Zhuravleva on datasets containing different types of source-target domain combinations to demonstrate the versatility and robustness of our method. We confirm that our method outperforms the state-of-the-art on all datasets.作者: 在前面 時間: 2025-3-28 14:22 作者: 立即 時間: 2025-3-28 14:36 作者: 一致性 時間: 2025-3-28 21:22 作者: collagen 時間: 2025-3-29 01:10
Unilateral Biportal Endoscopy of the Spinebaselines but also enhances annotation efficiency, offering potential few-shot segmentation capabilities with performance improvements ranging from 9% to 30% for segmentation tasks compared to SSL baselines. This performance is attributed to the significance of . via our learning strategy, which enc作者: Chemotherapy 時間: 2025-3-29 05:38 作者: Veneer 時間: 2025-3-29 10:36
Unilateral Biportal Endoscopy of the Spineefensive distillation for improved robustness against adversaries. Training using adversarial examples leads to better model generalizability and improves its ability to handle perturbations. Distillation using soft probabilities introduces uncertainty and variation into the output probabilities, ma作者: 無孔 時間: 2025-3-29 14:46 作者: 存在主義 時間: 2025-3-29 18:37 作者: Muscularis 時間: 2025-3-29 21:16 作者: 頭腦冷靜 時間: 2025-3-30 00:39
,MultiVT: Multiple-Task Framework for?Dentistry,obust features, we can achieve better segmentation and detection results. Additionally, MultiVT improves generalization capabilities without applying domain adaptive techniques - a characteristic which renders our method suitable for use in real-world applications.作者: 替代品 時間: 2025-3-30 04:07 作者: synovial-joint 時間: 2025-3-30 10:44
,Compositional Representation Learning for?Brain Tumour Segmentation, presence or absence of the tumour (or the tumour sub-regions) in the image are constructed. Then, vMFNet models the encoded image features with von-Mises-Fisher (vMF) distributions, via learnable and compositional vMF kernels which capture information about structures in the images. We show that go作者: hangdog 時間: 2025-3-30 13:45
,Realistic Data Enrichment for?Robust Image Segmentation in?Histopathology,egmentation of imbalanced objects within images. Therefore, we propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups by conditioning on segmentation maps. Our method can simply expand limited clinical datasets m作者: Diaphragm 時間: 2025-3-30 18:14 作者: Musket 時間: 2025-3-30 22:11 作者: pulmonary 時間: 2025-3-31 02:48 作者: 歡樂東方 時間: 2025-3-31 08:20
,SEDA: Self-ensembling ViT with?Defensive Distillation and?Adversarial Training for?Robust Chest X-Refensive distillation for improved robustness against adversaries. Training using adversarial examples leads to better model generalizability and improves its ability to handle perturbations. Distillation using soft probabilities introduces uncertainty and variation into the output probabilities, ma作者: GRIEF 時間: 2025-3-31 09:59 作者: Synchronism 時間: 2025-3-31 15:45
,Self-prompting Large Vision Models for?Few-Shot Medical Image Segmentation,s decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images). Our code is available at?作者: cyanosis 時間: 2025-3-31 19:47 作者: Canary 時間: 2025-4-1 00:36 作者: 聽寫 時間: 2025-4-1 04:24 作者: 委派 時間: 2025-4-1 09:49
Yoshio Hayasaki,Satoshi Hasegawanal network can improve segmentation results while being inexpensive in implementation as a nimble add-on to popular models. We hypothesize that this benefit of metadata can be attributed to facilitating multitask switching. This aspect of metadata-driven systems is explored and discussed in detail.
