標題: Titlebook: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning; Second MICCAI Worksh Shadi Albarqouni,Spyridon B [打印本頁] 作者: 矜持 時間: 2025-3-21 17:41
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning影響因子(影響力)
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning影響因子(影響力)學科排名
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning網(wǎng)絡公開度
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning網(wǎng)絡公開度學科排名
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning被引頻次
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning被引頻次學科排名
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning年度引用
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning年度引用學科排名
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning讀者反饋
書目名稱Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning讀者反饋學科排名
作者: Ascendancy 時間: 2025-3-21 21:36 作者: Motilin 時間: 2025-3-22 01:48
Nabanita Mukhopadhyay,Paramita De results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.作者: 使激動 時間: 2025-3-22 07:10
https://doi.org/10.1007/978-3-031-29422-8canner settings. We propose . (.nverse .istance .ggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known . approach, Federated Averaging as a baseline.作者: 總 時間: 2025-3-22 10:03
Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.作者: animated 時間: 2025-3-22 13:27 作者: animated 時間: 2025-3-22 17:59 作者: Synthesize 時間: 2025-3-22 22:45
G. Gupta,R. Shrivastava,J. Khan,N. K. Singhasets for autism detection and healthcare insurance. We compare with two methods and achieve state of the art performance in sensitive information leakage trade-off. A discussion regarding the difficulties of applying fair representation learning to medical data and when it is desirable is presented.作者: 激怒某人 時間: 2025-3-23 03:30 作者: Inveterate 時間: 2025-3-23 06:02 作者: MEAN 時間: 2025-3-23 10:56 作者: 熟練 時間: 2025-3-23 15:51
Conference proceedings 2020st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic.?..For DART 2020, 12 full papers were accepted from 18作者: prosthesis 時間: 2025-3-23 19:50 作者: 人類 時間: 2025-3-24 01:48 作者: 套索 時間: 2025-3-24 03:28
Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading the purpose of reducing unnecessary biopsies and diagnostic burden, we propose a patient-wise feature transfer model for learning the relationship of phenotypes between radiological images and pathological images. We hypothesize that high-level features from the same patient are possible to be link作者: Infelicity 時間: 2025-3-24 09:09
Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimagiious methods typically assume that multi-site data are sampled from the same distribution. Such an assumption may not hold in practice due to the data heterogeneity caused by different scanning parameters and subject populations in multiple imaging sites. Even though several deep domain adaptation m作者: 難聽的聲音 時間: 2025-3-24 12:49
Registration of Histopathology Images Using Self Supervised Fine Grained Feature Mapsration performance. We propose to integrate segmentation information in a registration framework using fine grained feature maps obtained in a self supervised manner. Self supervised feature maps enables use of segmentation information despite the unavailability of manual segmentations. Experimental作者: 違反 時間: 2025-3-24 16:20
Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Spacege of images from different modalities has great clinical benefits. However, the generalization ability of deep networks on different modalities is challenging due to domain shift. In this work, we investigate the challenging unsupervised domain adaptation problem of cross-modality medical image seg作者: 壓倒性勝利 時間: 2025-3-24 20:58
Semi-supervised Pathology Segmentation with Disentangled Representationsvised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatom作者: Orgasm 時間: 2025-3-24 23:11
Parts2Whole: Self-supervised Contrastive Learning via Reconstructionbanks, making it unappealing for 3D medical imaging, while in 3D medical imaging, reconstruction-based self-supervised learning reaches a new height in performance, but lacks mechanisms to learn contrastive representation; therefore, this paper proposes a new framework for self-supervised contrastiv作者: 灰姑娘 時間: 2025-3-25 04:58 作者: Licentious 時間: 2025-3-25 11:07
First U-Net Layers Contain More Domain Specific Information Than the Last Ones sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently proposed MRI domain adaptation methods operate with the last CNN layers to suppress domain shift. At the same time, the core manifestation of MRI variability is a considerable diversity of image inten作者: conception 時間: 2025-3-25 13:14
Siloed Federated Learning for Multi-centric Histopathology Datasets machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common. Building on previous domain adaptation works, this paper proposes a novel federated learning approach for deep learning architectures via the introduction of local-statistic batch norma作者: 噱頭 時間: 2025-3-25 16:15 作者: compassion 時間: 2025-3-25 23:21
Inverse Distance Aggregation for Federated Learning with Non-IID Data scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for . which is highly plausible in medical data where for example the data comes from different sites with different s作者: dissent 時間: 2025-3-26 03:11 作者: Clumsy 時間: 2025-3-26 06:39
Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizersifficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning with作者: erythema 時間: 2025-3-26 10:57 作者: moratorium 時間: 2025-3-26 15:17
https://doi.org/10.1007/978-3-031-25806-0 tasks. Despite improved performance, UNet++ introduces densely connected decoding blocks, some of which, however, are redundant for a specific task. In this paper, we propose .-UNet++ that allows us to automatically identify and discard redundant decoding blocks without the loss of precision. To th作者: 追逐 時間: 2025-3-26 17:46
https://doi.org/10.1007/978-3-031-25914-2arge amounts of sample images and precisely annotated labels, which is difficult to get in medical field. Domain adaptation can utilize limited labeled images of source domain to improve the performance of target domain. In this paper, we propose a novel domain adaptive predicting-refinement network作者: 凈禮 時間: 2025-3-26 23:58 作者: GAVEL 時間: 2025-3-27 01:45
Subhadeep Biswas,Ankurita Nath,Anjali Palious methods typically assume that multi-site data are sampled from the same distribution. Such an assumption may not hold in practice due to the data heterogeneity caused by different scanning parameters and subject populations in multiple imaging sites. Even though several deep domain adaptation m作者: 雇傭兵 時間: 2025-3-27 07:37 作者: 涂掉 時間: 2025-3-27 12:14
Marc Lunkenheimer,Alexander H. Kracklauerge of images from different modalities has great clinical benefits. However, the generalization ability of deep networks on different modalities is challenging due to domain shift. In this work, we investigate the challenging unsupervised domain adaptation problem of cross-modality medical image seg作者: 熱心 時間: 2025-3-27 14:45
Mirna Leko ?imi?,Helena ?timac,Sendi De?eli?vised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatom作者: Hallmark 時間: 2025-3-27 17:57 作者: 智力高 時間: 2025-3-28 01:33 作者: 脫離 時間: 2025-3-28 02:45
Sehoon Kwon,Jaechun No,Sung-soon Park sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently proposed MRI domain adaptation methods operate with the last CNN layers to suppress domain shift. At the same time, the core manifestation of MRI variability is a considerable diversity of image inten作者: 充氣球 時間: 2025-3-28 07:59 作者: 粗糙濫制 時間: 2025-3-28 14:27
G. Gupta,R. Shrivastava,J. Khan,N. K. Singhant against nuisance factors is an open question. This is done by removing sensitive information from the learned representation. Such privacy-preserving representations are believed to be beneficial to some medical and federated learning applications. In this paper, a framework for learning invaria作者: 果仁 時間: 2025-3-28 17:30 作者: 粉筆 時間: 2025-3-28 19:08 作者: 膽小懦夫 時間: 2025-3-29 02:54 作者: 運動吧 時間: 2025-3-29 05:51
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/282484.jpg作者: ablate 時間: 2025-3-29 08:22 作者: 愉快嗎 時間: 2025-3-29 13:26 作者: DIKE 時間: 2025-3-29 16:12 作者: Senescent 時間: 2025-3-29 19:50
Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging作者: 令人悲傷 時間: 2025-3-30 03:13
Continual Class Incremental Learning for CT Thoracic Segmentation作者: 防水 時間: 2025-3-30 04:20 作者: flaunt 時間: 2025-3-30 11:24 作者: patriarch 時間: 2025-3-30 16:03 作者: Indigence 時間: 2025-3-30 20:20 作者: Salivary-Gland 時間: 2025-3-30 22:58
Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading method to glioma grading (high-grade vs. low-grade) and train the feature transfer model by using patient-wise multimodal MRI images and pathological images. Evaluation results show that the proposed method can achieve pathological tumor grading score in high accuracy (AUC 0.959) only given the rad作者: LAST 時間: 2025-3-31 03:12 作者: GUILE 時間: 2025-3-31 05:50 作者: bifurcate 時間: 2025-3-31 09:22 作者: affect 時間: 2025-3-31 16:27 作者: 突襲 時間: 2025-3-31 19:50 作者: olfction 時間: 2025-3-31 23:28
