標題: Titlebook: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf; First MICCAI Worksho Qian Wang,Fausto [打印本頁] 作者: 誤解 時間: 2025-3-21 19:34
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf影響因子(影響力)
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf影響因子(影響力)學科排名
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf網(wǎng)絡公開度
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf網(wǎng)絡公開度學科排名
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf被引頻次
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf被引頻次學科排名
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf年度引用
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf年度引用學科排名
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf讀者反饋
書目名稱Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf讀者反饋學科排名
作者: 樂章 時間: 2025-3-21 23:04
Exploration of Legitimacy in East Asiao existing state-of-the-art networks with and without domain adaptation. Depending on the application, our method can improve multi-class classification accuracy by 5–20% compared to DANN introduced in [.].作者: 傷心 時間: 2025-3-22 01:55 作者: 滔滔不絕的人 時間: 2025-3-22 05:52 作者: 閃光你我 時間: 2025-3-22 10:07
Matthew D. Ostroff,Mark W. Connolly mechanism and dropout, while it does not increase parameters and computational costs, making it well-suited for small neuroimaging datasets. We evaluated our method on a challenging Traumatic Brain Injury (TBI) dataset collected from 13 sites, using labeled source data of only 14 . subjects. Experi作者: 法律 時間: 2025-3-22 15:59
Matthew D. Ostroff,Mark W. Connollyi-modal MRI samples with expert-derived lesion labels. We explore several transfer learning approaches to leverage the learned MS model for the task of multi-class brain tumor segmentation on the BraTS 2018 dataset. Our results indicate that adapting and fine-tuning the encoder and decoder of the ne作者: 法律 時間: 2025-3-22 18:43 作者: ARC 時間: 2025-3-23 00:54
Urban Living Lab for Local Regenerationhe public lumbar CT dataset. On the first dataset, WISS achieves distinct improvements with regard to two different backbones. For the second dataset, WISS achieves dice coefficients of . and . for mid-sagittal slices and 3D CT volumes, respectively, saving a lot of labeling costs and only sacrifici作者: LIMN 時間: 2025-3-23 02:42
https://doi.org/10.1007/978-3-031-19748-2patial attention and channel attention blocks for capturing the high-level feature map’s long-range dependencies and helps to synthesize a more semantic-consistent feature map, and thereby boosting weakly-supervised lesion localization and classification performance; Secondly, a multi-channel dilate作者: 鞠躬 時間: 2025-3-23 06:04
Urban Living Lab for Local Regeneration delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a data作者: 職業(yè) 時間: 2025-3-23 12:34 作者: GRAVE 時間: 2025-3-23 17:46
Temporal Consistency Objectives Regularize the Learning of Disentangled Representationsrove semi-supervised segmentation, especially when very few labelled data are available. Specifically, we show Dice increase of up?to 19% and 7% compared to supervised and semi-supervised approaches respectively on the ACDC dataset. Code is available at: ..作者: optic-nerve 時間: 2025-3-23 20:03 作者: 引導 時間: 2025-3-23 23:21 作者: nonplus 時間: 2025-3-24 02:40 作者: 氣候 時間: 2025-3-24 09:32
Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site T mechanism and dropout, while it does not increase parameters and computational costs, making it well-suited for small neuroimaging datasets. We evaluated our method on a challenging Traumatic Brain Injury (TBI) dataset collected from 13 sites, using labeled source data of only 14 . subjects. Experi作者: 現(xiàn)存 時間: 2025-3-24 12:39
Improving Pathological Structure Segmentation via Transfer Learning Across Diseasesi-modal MRI samples with expert-derived lesion labels. We explore several transfer learning approaches to leverage the learned MS model for the task of multi-class brain tumor segmentation on the BraTS 2018 dataset. Our results indicate that adapting and fine-tuning the encoder and decoder of the ne作者: Antagonism 時間: 2025-3-24 17:01
Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performancons. We also compared the localization and classification performance with and without image augmentation by using generated VIC images. Our results show that the model trained on IC and VIC images had the highest performance in both localization and classification. Therefore, VIC images are useful 作者: 保守 時間: 2025-3-24 21:46
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagationhe public lumbar CT dataset. On the first dataset, WISS achieves distinct improvements with regard to two different backbones. For the second dataset, WISS achieves dice coefficients of . and . for mid-sagittal slices and 3D CT volumes, respectively, saving a lot of labeling costs and only sacrifici作者: 伴隨而來 時間: 2025-3-24 23:45 作者: malign 時間: 2025-3-25 06:50
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a data作者: Entropion 時間: 2025-3-25 11:28 作者: cardiovascular 時間: 2025-3-25 12:42
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and ImperfFirst MICCAI Worksho作者: 果仁 時間: 2025-3-25 16:16 作者: iodides 時間: 2025-3-25 20:45
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations require explainability, whilst relying less on annotated data (since annotations can be tedious and costly). Here we build on recent innovations in style-content representations to learn anatomy, imaging characteristics (appearance) and temporal correlations. By introducing a self-supervised object作者: 意外 時間: 2025-3-26 02:50
Multi-layer Domain Adaptation for Deep Convolutional Networksurthermore, the performance is not guaranteed on a sample from an unseen domain at test time, if the network was not exposed to similar samples from that domain at training time. This hinders the adoption of these techniques in clinical setting where the imaging data is scarce, and where the intra- 作者: 思鄉(xiāng)病 時間: 2025-3-26 07:06
Intramodality Domain Adaptation Using Self Ensembling and Adversarial Trainingades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift betwe作者: Constant 時間: 2025-3-26 10:34 作者: Pastry 時間: 2025-3-26 14:00
Synthesising Images and Labels Between MR Sequence Types with CycleGANubjects unable to hold the breath or suffering from arrhythmia. RT image acquisitions during free breathing produce comparatively poor quality images, a trade-off necessary to achieve the high temporal resolution needed for RT imaging and hence are less suitable in the clinical assessment of cardiac作者: 大炮 時間: 2025-3-26 17:49 作者: 殺蟲劑 時間: 2025-3-26 23:48 作者: emulsify 時間: 2025-3-27 04:11 作者: Anecdote 時間: 2025-3-27 05:49
Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Timages are often collected from multiple sites and/or protocols for increasing statistical power, while CNN trained on one site typically cannot be well-transferred to others. Further, expert-defined manual labels for medical images are typically rare, making training a dedicated CNN for each site u作者: 同義聯(lián)想法 時間: 2025-3-27 10:16 作者: 寬度 時間: 2025-3-27 14:34 作者: Abbreviate 時間: 2025-3-27 20:44 作者: 敲竹杠 時間: 2025-3-27 23:05 作者: 粗俗人 時間: 2025-3-28 02:55 作者: Brain-Waves 時間: 2025-3-28 06:28
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CTting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital picture archiving and communication systems (PACSs). This is the focus of our work, where we present a principled data curation tool to extract multi-phase compu作者: 妨礙議事 時間: 2025-3-28 13:22
Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Imagesntation. However, deep learning requires the availability of large annotated data to train these models, which can be challenging in biomedical imaging domain. In this paper, we aim to accomplish biomedical image segmentation with limited labeled data using active learning. We present a deep active 作者: interrupt 時間: 2025-3-28 17:59
0302-9743 d the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. ..DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancem作者: ELATE 時間: 2025-3-28 19:21
Understanding Legitimacy in Criminal Justiceluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model’s feature maps reveals that it has learned to distinguish retinal layers and other distinct regions of the images.作者: sleep-spindles 時間: 2025-3-29 01:15 作者: 潔凈 時間: 2025-3-29 04:51
0302-9743 istent across different domains. ..MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.?.978-3-030-33390-4978-3-030-33391-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Noctambulant 時間: 2025-3-29 10:24 作者: 浪費物質 時間: 2025-3-29 13:57
Software Product Line Architecturesgled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of . in terms of disentanglement, . in clustering, and . in supervised classification with a few amount of labeled data.作者: Libido 時間: 2025-3-29 17:28 作者: 舔食 時間: 2025-3-29 21:06
https://doi.org/10.1007/978-3-031-18583-0 facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.作者: jungle 時間: 2025-3-30 01:30
Palgrave Studies in Cultural Participationach to training inverse models in medical imaging in the absence of aligned data. Our method only requiring access to the measurements and the forward model at training. We showcase its effectiveness on inverse problems arising in accelerated magnetic resonance imaging (MRI).作者: 抱狗不敢前 時間: 2025-3-30 05:49 作者: panorama 時間: 2025-3-30 08:16
Learning Interpretable Disentangled Representations Using Adversarial VAEsgled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of . in terms of disentanglement, . in clustering, and . in supervised classification with a few amount of labeled data.作者: 熱情的我 時間: 2025-3-30 14:17 作者: Heart-Attack 時間: 2025-3-30 16:40
A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.作者: 聯(lián)合 時間: 2025-3-30 21:38 作者: Carcinogenesis 時間: 2025-3-31 03:51
Conference proceedings 2019t International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. ..DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and i作者: Dislocation 時間: 2025-3-31 06:22 作者: 熱心 時間: 2025-3-31 11:02
https://doi.org/10.1007/978-3-030-33391-1artificial intelligence; ct image; image analysis; image reconstruction; image segmentation; imaging syst
