書(shū)目名稱Domain Adaptation and Representation Transfer影響因子(影響力)學(xué)科排名
書(shū)目名稱Domain Adaptation and Representation Transfer網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Domain Adaptation and Representation Transfer網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Domain Adaptation and Representation Transfer被引頻次
書(shū)目名稱Domain Adaptation and Representation Transfer被引頻次學(xué)科排名
書(shū)目名稱Domain Adaptation and Representation Transfer年度引用
書(shū)目名稱Domain Adaptation and Representation Transfer年度引用學(xué)科排名
書(shū)目名稱Domain Adaptation and Representation Transfer讀者反饋
書(shū)目名稱Domain Adaptation and Representation Transfer讀者反饋學(xué)科排名
作者: 無(wú)效 時(shí)間: 2025-3-21 21:35 作者: 擁護(hù)者 時(shí)間: 2025-3-22 00:37 作者: ROOF 時(shí)間: 2025-3-22 08:13 作者: 發(fā)芽 時(shí)間: 2025-3-22 12:20
,Task-Agnostic Continual Hippocampus Segmentation for?Smooth Population Shifts,adual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.作者: Paradox 時(shí)間: 2025-3-22 15:13
Conference proceedings 2022orum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains..?.作者: Paradox 時(shí)間: 2025-3-22 17:48
0302-9743 scussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains..?.978-3-031-16851-2978-3-031-16852-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 殘忍 時(shí)間: 2025-3-23 00:54 作者: jocular 時(shí)間: 2025-3-23 04:49
Conference proceedings 2022tion with MICCAI 2022, in September 2022.?.DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning作者: Impugn 時(shí)間: 2025-3-23 06:01
,Unsupervised Site Adaptation by?Intra-site Variability Alignment,and propose an . method that jointly aligns the intra-site data variability in the source and target sites while training the network on the labeled source site data. We applied our method to several medical MRI image segmentation tasks and show that it consistently outperforms state-of-the-art methods.作者: invert 時(shí)間: 2025-3-23 10:49 作者: Endemic 時(shí)間: 2025-3-23 14:32
René Sotelo,Charles F. Polotti,Juan Arriagawo schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the . with different levels of data scarcity from the target site, on multiple datasets and tasks.作者: Peak-Bone-Mass 時(shí)間: 2025-3-23 21:25 作者: LAIR 時(shí)間: 2025-3-23 23:39 作者: MIRTH 時(shí)間: 2025-3-24 03:51
,Supervised Domain Adaptation Using Gradients Transfer for?Improved Medical Image Analysis,wo schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the . with different levels of data scarcity from the target site, on multiple datasets and tasks.作者: ILEUM 時(shí)間: 2025-3-24 06:39 作者: discord 時(shí)間: 2025-3-24 12:53
0302-9743 in conjunction with MICCAI 2022, in September 2022.?.DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/dee作者: GET 時(shí)間: 2025-3-24 17:08 作者: Fulsome 時(shí)間: 2025-3-24 20:45
,Detecting Melanoma Fairly: Skin Tone Detection and?Debiasing for?Skin Lesion Classification,nce disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use these auto作者: RACE 時(shí)間: 2025-3-24 23:29
,Benchmarking and Boosting Transformers for?Medical Image Classification,one representative visual benchmark after another. However, the competition between visual transformers and CNNs in medical imaging is rarely studied, leaving many important questions unanswered. As the first step, we benchmark how well existing transformer variants that use various (supervised and 作者: 廣口瓶 時(shí)間: 2025-3-25 03:26
,Supervised Domain Adaptation Using Gradients Transfer for?Improved Medical Image Analysis,d Domain Adaptation (SDA) strategies that focus on this challenge, assume the availability of a limited number of annotated samples from the new site. A typical SDA approach is to pre-train the model on the source site and then fine-tune on the target site. Current research has thus mainly focused o作者: 公共汽車(chē) 時(shí)間: 2025-3-25 10:54 作者: 陶醉 時(shí)間: 2025-3-25 13:39 作者: 間諜活動(dòng) 時(shí)間: 2025-3-25 18:40
,Unsupervised Site Adaptation by?Intra-site Variability Alignment,ferent target domain. This is known as the domain-shift problem. In this study, we propose a general method for transfer knowledge from a source site with labeled data to a target site where only unlabeled data is available. We leverage the variability that is often present within each site, the ., 作者: 摘要記錄 時(shí)間: 2025-3-25 21:27 作者: 碌碌之人 時(shí)間: 2025-3-26 00:20 作者: 引導(dǎo) 時(shí)間: 2025-3-26 07:09
,Feather-Light Fourier Domain Adaptation in?Magnetic Resonance Imaging,en the sets are produced by different hardware. As a consequence of this ., a certain model might perform well on data from one clinic, and then fail when deployed in another. We propose a very light and transparent approach to perform .. The idea is to substitute the . low-frequency Fourier space c作者: 易發(fā)怒 時(shí)間: 2025-3-26 10:34
,Seamless Iterative Semi-supervised Correction of?Imperfect Labels in?Microscopy Images,growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose ., a new method for trainin作者: 鈍劍 時(shí)間: 2025-3-26 13:23
,Task-Agnostic Continual Hippocampus Segmentation for?Smooth Population Shifts,training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validati作者: SHRIK 時(shí)間: 2025-3-26 18:14
,Adaptive Optimization with?Fewer Epochs Improves Across-Scanner Generalization of?U-Net Based Medic are trained on images that have been acquired with a specific scanner, and are applied to images from another scanner. This indicates an overfitting to image characteristics that are irrelevant to the semantic contents, and is usually mitigated with data augmentation. We argue that early stopping a作者: 慌張 時(shí)間: 2025-3-26 23:01
CateNorm: Categorical Normalization for Robust Medical Image Segmentation, of the background pixels often dominates the BN statistics because the background accounts for a large proportion of the entire image. This paper focuses on enhancing BN with the intensity distribution of foreground pixels, the one that really matters for image segmentation. We propose a new normal作者: Visual-Acuity 時(shí)間: 2025-3-27 01:28
Domain Adaptation and Representation Transfer978-3-031-16852-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Ambiguous 時(shí)間: 2025-3-27 05:56
Vesicovaginal Fistula: Open Approachnce disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use these auto作者: CHASM 時(shí)間: 2025-3-27 10:45 作者: 濕潤(rùn) 時(shí)間: 2025-3-27 16:36 作者: 木質(zhì) 時(shí)間: 2025-3-27 21:18 作者: milligram 時(shí)間: 2025-3-28 00:46
Treatment of the Ureteral Lesionce, leading to poor model training convergence, while other organs have plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal of capturing the variety between the sli作者: 多樣 時(shí)間: 2025-3-28 04:21 作者: 跟隨 時(shí)間: 2025-3-28 09:12 作者: 消瘦 時(shí)間: 2025-3-28 10:54
Why and How to Restrict Freedomnnotated photographic images. However, their acceptance in medical imaging is still lukewarm, due to the significant discrepancy between medical and photographic images. Consequently, we propose POPAR (patch order prediction and appearance recovery), a novel vision transformer-based self-supervised 作者: 有抱負(fù)者 時(shí)間: 2025-3-28 16:05 作者: 向下 時(shí)間: 2025-3-28 21:16
https://doi.org/10.1007/978-3-031-16640-2growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose ., a new method for trainin作者: Ventricle 時(shí)間: 2025-3-29 00:44
Understanding Workplace Relationshipstraining and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validati作者: 愚蠢人 時(shí)間: 2025-3-29 06:36 作者: Harbor 時(shí)間: 2025-3-29 10:42
Ajay Mehra,Diane Kang,Evgenia Dolgova of the background pixels often dominates the BN statistics because the background accounts for a large proportion of the entire image. This paper focuses on enhancing BN with the intensity distribution of foreground pixels, the one that really matters for image segmentation. We propose a new normal作者: 合同 時(shí)間: 2025-3-29 11:27 作者: Scintigraphy 時(shí)間: 2025-3-29 19:25 作者: precede 時(shí)間: 2025-3-29 20:06 作者: medium 時(shí)間: 2025-3-30 03:57
,Benchmarking and Boosting Transformers for?Medical Image Classification, imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge th作者: 噱頭 時(shí)間: 2025-3-30 07:43 作者: 極少 時(shí)間: 2025-3-30 10:33
,Discriminative, Restorative, and?Adversarial Learning: Stepwise Incremental Pretraining,torative learning, and finally, the pretrained encoder-decoder is associated with an adversarial encoder for final full discriminative, restorative, and adversarial learning. Our extensive experiments demonstrate that the stepwise incremental pretraining stabilizes United models training, resulting 作者: Apogee 時(shí)間: 2025-3-30 14:24 作者: 輪流 時(shí)間: 2025-3-30 16:45
,Seamless Iterative Semi-supervised Correction of?Imperfect Labels in?Microscopy Images,ssfully provides an adaptive early learning correction technique for object detection. The combination of early learning correction that has been applied in classification and semantic segmentation before and synthetic-like image generation proves to be more effective than the usual semi-supervised 作者: Catheter 時(shí)間: 2025-3-31 00:14
,Adaptive Optimization with?Fewer Epochs Improves Across-Scanner Generalization of?U-Net Based Medic, by training for only 50 epochs with AvaGrad, and to exceed their results in the across-scanner setting. This benefit is specific to combining adaptive optimization and early stopping, since it can be matched neither by SGD with a low number of epochs, nor by Avagrad with many epochs. Finally, we d作者: 離開(kāi)就切除 時(shí)間: 2025-3-31 00:58
Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftari作者: stress-response 時(shí)間: 2025-3-31 05:38
Preston K. Kerr,Steven B. Brandes imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge th作者: 收集 時(shí)間: 2025-3-31 10:51
Treatment of the Ureteral Lesionsults show that our proposed volumetric task definition leads to up to . improvement in terms of IoU compared to related baselines. The proposed update rule is also shown to improve the performance for complex scenarios where the data distribution of the target organ is very different from the sourc
