標(biāo)題: Titlebook: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse; Third MICCAI Worksho Shadi Albarqouni [打印本頁(yè)] 作者: Interpolate 時(shí)間: 2025-3-21 16:51
書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse影響因子(影響力)
書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse影響因子(影響力)學(xué)科排名
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書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse讀者反饋
書目名稱Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse讀者反饋學(xué)科排名
作者: thyroid-hormone 時(shí)間: 2025-3-21 22:12
Francesco Alberti,Antonella Radicchiing aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multi-scale consistency loss, wh作者: FAST 時(shí)間: 2025-3-22 03:14 作者: 槍支 時(shí)間: 2025-3-22 08:19
Gakwaya P. Isingizwe,Giuseppe T. Cirellal learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting, i.e., a decrease in performance on the data encountered earlier. This issue makes continuous training of segmentation models for作者: Root494 時(shí)間: 2025-3-22 09:00 作者: geometrician 時(shí)間: 2025-3-22 13:13 作者: geometrician 時(shí)間: 2025-3-22 19:03 作者: 有組織 時(shí)間: 2025-3-23 00:33 作者: intricacy 時(shí)間: 2025-3-23 01:46
https://doi.org/10.1007/978-3-031-23759-1geneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel spac作者: 檔案 時(shí)間: 2025-3-23 06:46 作者: phase-2-enzyme 時(shí)間: 2025-3-23 12:29 作者: hermitage 時(shí)間: 2025-3-23 14:57
https://doi.org/10.1007/978-3-031-23759-1asets become publicly available, the number of samples in each individual database is often small. Combining different databases to create larger amounts of training data is appealing yet challenging due to the heterogeneity as a result of differences in data acquisition and annotation processes, of作者: Inscrutable 時(shí)間: 2025-3-23 22:04 作者: 圣人 時(shí)間: 2025-3-23 23:36
Madhubarna Dhar,Amrita Sen,Archana Patnaikspecific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc.) and different blood film slides preparation. Generative adve作者: Addictive 時(shí)間: 2025-3-24 03:35 作者: 星星 時(shí)間: 2025-3-24 08:51 作者: 我不怕犧牲 時(shí)間: 2025-3-24 13:56 作者: BADGE 時(shí)間: 2025-3-24 18:55 作者: 松軟無(wú)力 時(shí)間: 2025-3-24 19:25 作者: notice 時(shí)間: 2025-3-25 01:10 作者: 放逐 時(shí)間: 2025-3-25 04:07 作者: 使人煩燥 時(shí)間: 2025-3-25 08:57
A Systematic Benchmarking Analysis of?Transfer Learning for Medical Image?Analysisd to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale 作者: 失誤 時(shí)間: 2025-3-25 15:04 作者: 檢查 時(shí)間: 2025-3-25 17:49
FDA: Feature Decomposition and?Aggregation for Robust Airway Segmentationset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes作者: lattice 時(shí)間: 2025-3-25 23:35 作者: 蓋他為秘密 時(shí)間: 2025-3-26 00:43 作者: 都相信我的話 時(shí)間: 2025-3-26 05:37
Self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentationased segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. Manual segmentation 作者: Wernickes-area 時(shí)間: 2025-3-26 08:34
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training of many computer vision methods, including those developed for medical image segmentation. These methods jointly train a segmentor and an adversarial mask discriminator, which provides a data-driven shape prior. At inference, the discriminator is discarded, and only the segmentor is used to predict作者: constitute 時(shí)間: 2025-3-26 13:09
Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimationsed mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracti作者: aphasia 時(shí)間: 2025-3-26 17:36
Unsupervised Domain Adaptation with Semantic Consistency Across Heterogeneous Modalities for MRI Progeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel spac作者: hauteur 時(shí)間: 2025-3-27 00:54
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentationalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts. One strategy to create a more diverse, generalizable training set is to naively pool datasets from different cohorts. Surprisingly, training on this . does not necessarily increase, and may even red作者: Kidnap 時(shí)間: 2025-3-27 03:05 作者: 巨頭 時(shí)間: 2025-3-27 06:04 作者: Atrium 時(shí)間: 2025-3-27 09:40
Unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentationn applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaption attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a novel framework for cross-modality segmentation via similarity-based prototypes. In specific, we作者: 傳授知識(shí) 時(shí)間: 2025-3-27 13:35
Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc.) and different blood film slides preparation. Generative adve作者: Encapsulate 時(shí)間: 2025-3-27 17:56
Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, h作者: forecast 時(shí)間: 2025-3-27 23:21 作者: expire 時(shí)間: 2025-3-28 03:32 作者: 協(xié)迫 時(shí)間: 2025-3-28 06:44 作者: detach 時(shí)間: 2025-3-28 11:20
Conference proceedings 2021onsistent across different domains. ..For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning...?..?.作者: Mitigate 時(shí)間: 2025-3-28 14:44
https://doi.org/10.1007/978-3-031-20995-6ations that are more suitable for medical segmentation tasks, (2) self-supervised ImageNet models learn holistic features more effectively than supervised ImageNet models, and (3) continual pre-training can bridge the domain gap between natural and medical images. We hope that this large-scale open 作者: 終端 時(shí)間: 2025-3-28 22:28 作者: 沙文主義 時(shí)間: 2025-3-29 01:07 作者: 結(jié)果 時(shí)間: 2025-3-29 05:31 作者: 不足的東西 時(shí)間: 2025-3-29 08:32
https://doi.org/10.1007/978-3-031-23629-7 test instance until it satisfies the learned shape prior. Our method is simple to implement and increases model performance. Moreover, it opens new directions for re-using mask discriminators at inference. We release the code used for the experiments at?..作者: 不規(guī)則 時(shí)間: 2025-3-29 11:41 作者: unstable-angina 時(shí)間: 2025-3-29 17:39
https://doi.org/10.1007/978-3-031-23759-1o extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approac作者: Ceremony 時(shí)間: 2025-3-29 21:23 作者: JAMB 時(shí)間: 2025-3-29 23:52 作者: PATHY 時(shí)間: 2025-3-30 05:59 作者: 門窗的側(cè)柱 時(shí)間: 2025-3-30 09:42
Urbicide: Towards a Conceptualizationtime-dependent evaluation trajectory of a brain graph from a single baseline. Our RBGM contains a set of .-inspired mappers for each time point, where each mapper aims to project the ground-truth brain graph onto its next time point. We leverage the teacher forcing method to boost training and impro作者: athlete’s-foot 時(shí)間: 2025-3-30 15:54 作者: Itinerant 時(shí)間: 2025-3-30 16:33
FDA: Feature Decomposition and?Aggregation for Robust Airway Segmentatione SDM pays more attention to the bronchi, which is beneficial to extracting the transferable topological features robust to the coarse labels. Extensive experimental results demonstrated the obvious improvement brought by our proposed method. Compared to other state-of-the-art transfer learning meth
