標(biāo)題: Titlebook: Data Augmentation, Labelling, and Imperfections; Third MICCAI Worksho Yuan Xue,Chen Chen,Yihao Liu Conference proceedings 2024 The Editor(s [打印本頁(yè)] 作者: 使無(wú)罪 時(shí)間: 2025-3-21 16:05
書(shū)目名稱Data Augmentation, Labelling, and Imperfections影響因子(影響力)
書(shū)目名稱Data Augmentation, Labelling, and Imperfections影響因子(影響力)學(xué)科排名
書(shū)目名稱Data Augmentation, Labelling, and Imperfections網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Data Augmentation, Labelling, and Imperfections網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Data Augmentation, Labelling, and Imperfections被引頻次
書(shū)目名稱Data Augmentation, Labelling, and Imperfections被引頻次學(xué)科排名
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書(shū)目名稱Data Augmentation, Labelling, and Imperfections年度引用學(xué)科排名
書(shū)目名稱Data Augmentation, Labelling, and Imperfections讀者反饋
書(shū)目名稱Data Augmentation, Labelling, and Imperfections讀者反饋學(xué)科排名
作者: 笨重 時(shí)間: 2025-3-22 00:08
,Zero-Shot Learning of?Individualized Task Contrast Prediction from?Resting-State Functional Connect large language models using special inputs to obtain answers for novel natural language processing tasks, inputting group-average contrasts guides the OPIC?model to generalize to novel tasks unseen in training. Experimental results show that OPIC’s predictions for novel tasks are not only better th作者: A簡(jiǎn)潔的 時(shí)間: 2025-3-22 01:17
,Microscopy Image Segmentation via?Point and?Shape Regularized Data Synthesis,d by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining hi作者: 旅行路線 時(shí)間: 2025-3-22 04:45
,A Unified Approach to?Learning with?Label Noise and?Unsupervised Confidence Approximation,datasets. UCA’s prediction accuracy increases with the required level of confidence. UCA-equipped networks are on par with the state-of-the-art in noisy label training when used in regular, full coverage mode. However, they have a risk-management facility, showing flawless risk-coverage curves with 作者: 舉止粗野的人 時(shí)間: 2025-3-22 12:40
Transesophageal Echocardiography Generation Using Anatomical Models,ynthetic images quantitatively using the Fréchet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher..In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic ima作者: Fibroid 時(shí)間: 2025-3-22 16:44
,Data Augmentation Based on?DiscrimDiff for?Histopathology Image Classification,ing significance for pathologists in clinical diagnosis. Therefore, we visualize histomorphological features related to classification, which can be used to assist pathologist-in-training education and improve the understanding of histomorphology.作者: Fibroid 時(shí)間: 2025-3-22 20:55 作者: 清真寺 時(shí)間: 2025-3-22 21:19
,Knowledge Graph Embeddings for?Multi-lingual Structured Representations of?Radiology Reports,ly more accurate, without reliance on large pre-training datasets. We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification. For disease classification our model is competitive with its BERT-based counterparts, while being magnitudes smal作者: 招募 時(shí)間: 2025-3-23 01:51
,Masked Conditional Diffusion Models for?Image Analysis with?Application to?Radiographic Diagnosis ombines the weighted segmentation masks of the tibias and the CML fracture sites as additional conditions for classifier guidance. The augmented images from our model improved the performances of ResNet-34 in classifying normal radiographs and those with CMLs. Further, the augmented images and their 作者: AROMA 時(shí)間: 2025-3-23 06:17 作者: synovitis 時(shí)間: 2025-3-23 13:21 作者: 平項(xiàng)山 時(shí)間: 2025-3-23 14:53 作者: Optic-Disk 時(shí)間: 2025-3-23 18:53 作者: Flat-Feet 時(shí)間: 2025-3-24 00:22 作者: 的染料 時(shí)間: 2025-3-24 03:39
Nutrient Management Under Changing Climateynthetic images quantitatively using the Fréchet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher..In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic ima作者: FOR 時(shí)間: 2025-3-24 06:58
Mohamed A. M. Osman,Mohamed A. Shebling significance for pathologists in clinical diagnosis. Therefore, we visualize histomorphological features related to classification, which can be used to assist pathologist-in-training education and improve the understanding of histomorphology.作者: 反復(fù)無(wú)常 時(shí)間: 2025-3-24 14:04
https://doi.org/10.1007/978-3-030-41629-4respect to their detection and localisation accuracy, by assigning the corresponding report sentence where a clinically relevant anomaly is correctly detected, and rating localisation according to a 3-point scale (good, partial, poor). We find that neither method exhibits sufficiently high recall fo作者: 是他笨 時(shí)間: 2025-3-24 15:23
Tsugihiro Watanabe,Selim Kapur,Erhan Ak?aly more accurate, without reliance on large pre-training datasets. We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification. For disease classification our model is competitive with its BERT-based counterparts, while being magnitudes smal作者: squander 時(shí)間: 2025-3-24 22:37
Upendra Kumar,Subhra Parija,Megha Kavirajmbines the weighted segmentation masks of the tibias and the CML fracture sites as additional conditions for classifier guidance. The augmented images from our model improved the performances of ResNet-34 in classifying normal radiographs and those with CMLs. Further, the augmented images and their 作者: 搖擺 時(shí)間: 2025-3-24 23:37 作者: 性上癮 時(shí)間: 2025-3-25 03:27
,URL: Combating Label Noise for?Lung Nodule Malignancy Grading,y degrades the performance and generalizability of models. Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task. To model the ordinal relation among classes to作者: 建筑師 時(shí)間: 2025-3-25 08:14 作者: Nerve-Block 時(shí)間: 2025-3-25 14:16 作者: 種類 時(shí)間: 2025-3-25 19:51 作者: engrossed 時(shí)間: 2025-3-25 23:44
Transesophageal Echocardiography Generation Using Anatomical Models,ounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an 作者: 逢迎春日 時(shí)間: 2025-3-26 01:41 作者: 親密 時(shí)間: 2025-3-26 08:20 作者: calumniate 時(shí)間: 2025-3-26 12:02 作者: NOCT 時(shí)間: 2025-3-26 15:49 作者: 向宇宙 時(shí)間: 2025-3-26 19:19
,Modular, Label-Efficient Dataset Generation for?Instrument Detection for?Robotic Scrub Nurses, demand large amounts of annotated data, whose creation is expensive and time-consuming. In this work, we propose a strategy based on the copy-paste technique for the generation of reliable synthetic image training data with a minimal amount of annotation effort. Our approach enables the efficient i作者: 聰明 時(shí)間: 2025-3-26 23:40
,Adaptive Semi-supervised Segmentation of?Brain Vessels with?Ambiguous Labels,pturing small vessels and handling datasets that are partially or ambiguously annotated. In this paper, we propose an adaptive semi-supervised approach to address these challenges. Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training str作者: 提煉 時(shí)間: 2025-3-27 02:16 作者: 增長(zhǎng) 時(shí)間: 2025-3-27 06:21 作者: 多骨 時(shí)間: 2025-3-27 13:25 作者: Maximize 時(shí)間: 2025-3-27 15:03
,Masked Conditional Diffusion Models for?Image Analysis with?Application to?Radiographic Diagnosis oiologists detect these subtle fractures, we need to develop a model that can flag abnormal distal tibial radiographs (i.e. those with CMLs). Unfortunately, the development of such a model requires a large and diverse training database, which is often not available. To address this limitation, we pro作者: 會(huì)犯錯(cuò)誤 時(shí)間: 2025-3-27 21:18
,Self-supervised Single-Image Deconvolution with?Siamese Neural Networks,fy noise and require careful parameter selection for an optimal trade-off between sharpness and grain. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. Recently, self-supervised blind-spot neural networks were successfully adop作者: 只有 時(shí)間: 2025-3-28 01:01 作者: FAZE 時(shí)間: 2025-3-28 03:33 作者: MANIA 時(shí)間: 2025-3-28 06:44
Climate Change and Animal Farmingrediction of nodule presence on a clinical ultrasound dataset. The results on this as well as two other medical image datasets suggest that even successful active learning strategies have limited clinical significance in terms of reducing annotation burden.作者: humectant 時(shí)間: 2025-3-28 13:33
Debarup Das,Prasenjit Ray,S. P. Dattaitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.作者: Brocas-Area 時(shí)間: 2025-3-28 17:29 作者: CREST 時(shí)間: 2025-3-28 20:21 作者: Increment 時(shí)間: 2025-3-28 23:03
,Active Learning Strategies on?a?Real-World Thyroid Ultrasound Dataset,rediction of nodule presence on a clinical ultrasound dataset. The results on this as well as two other medical image datasets suggest that even successful active learning strategies have limited clinical significance in terms of reducing annotation burden.作者: Offbeat 時(shí)間: 2025-3-29 05:59
A Realistic Collimated X-Ray Image Simulation Pipeline,itative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.作者: Lineage 時(shí)間: 2025-3-29 09:52
Conference proceedings 202423), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International..Conference on Medical Image Computing and Computer Assisted Intervention..(MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions...The conferen作者: 重畫(huà)只能放棄 時(shí)間: 2025-3-29 11:35 作者: sterilization 時(shí)間: 2025-3-29 15:48
Conference proceedings 2024ce fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis..作者: BOLT 時(shí)間: 2025-3-29 23:36
0302-9743 e conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis..978-3-031-58170-0978-3-031-58171-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: JADED 時(shí)間: 2025-3-29 23:53 作者: BILIO 時(shí)間: 2025-3-30 04:06 作者: 白楊 時(shí)間: 2025-3-30 08:56 作者: Tractable 時(shí)間: 2025-3-30 15:16
,Modular, Label-Efficient Dataset Generation for?Instrument Detection for?Robotic Scrub Nurses,on synthetic data, in a real-world scenario. Our evaluation relies on an annotated image dataset of the wisdom teeth extraction surgery set, created in an actual operating room. This dataset, the corresponding code, and further data are made publicly available (.).作者: contradict 時(shí)間: 2025-3-30 18:25
https://doi.org/10.1007/978-3-031-58171-7artificial intelligence; bioinformatics; color image processing; color images; computer systems; computer作者: 迫擊炮 時(shí)間: 2025-3-30 23:41
978-3-031-58170-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 恃強(qiáng)凌弱的人 時(shí)間: 2025-3-31 02:26
Data Augmentation, Labelling, and Imperfections978-3-031-58171-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: –scent 時(shí)間: 2025-3-31 07:27 作者: 有罪 時(shí)間: 2025-3-31 09:52
