標(biāo)題: Titlebook: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections; First Workshop, DGM4 Sandy Engelhardt,Ilkay Oksuz,Yuan Xue Con [打印本頁] 作者: 貪求 時(shí)間: 2025-3-21 19:44
書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections影響因子(影響力)
書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections影響因子(影響力)學(xué)科排名
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書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections被引頻次
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書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections讀者反饋
書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections讀者反饋學(xué)科排名
作者: 厚顏 時(shí)間: 2025-3-21 23:29
Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domainent imaging settings. We address these problems using a novel variational style-transfer neural network that can sample various styles from a computed latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmenta作者: 費(fèi)解 時(shí)間: 2025-3-22 02:26 作者: Amylase 時(shí)間: 2025-3-22 08:07 作者: 暗語 時(shí)間: 2025-3-22 08:45
Conditional Generation of Medical Images via Disentangled Adversarial Inferencentations of style and content, and use this information to impose control over conditional generation process. We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of 作者: Nefarious 時(shí)間: 2025-3-22 15:03
CT-SGAN: Computed Tomography Synthesis GAN the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes (.) when trained on a small dataset of chest CT-scans. CT-SGAN offers an attractive solution to two major challenges facing machine learning in medical imaging: a small number of given i.i.d. training data, and the restrictio作者: Nefarious 時(shí)間: 2025-3-22 18:47 作者: placebo-effect 時(shí)間: 2025-3-22 23:10
CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patternsons (classification), as biomedical images naturally contain image-wise labels in many scenarios. The current weakly supervised learning algorithms from the computer vision community are largely designed for focal objects (e.g., dogs and cats). However, such algorithms are not optimized for diffuse 作者: vascular 時(shí)間: 2025-3-23 02:16
BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dement remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a non-invasive biomarker to detect brain aging. Previous evidence shows that the structural brain network generated from the diffusion MRI promises to classify dementia accurately based on d作者: entreat 時(shí)間: 2025-3-23 08:04 作者: jagged 時(shí)間: 2025-3-23 11:01
Improved Heatmap-Based Landmark Detectionation of heart function. The location of the prosthesis’ sutures is critical. Obtaining and studying them during the procedure is a valuable learning experience for new surgeons. This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem o作者: 執(zhí) 時(shí)間: 2025-3-23 17:11
Cross-Domain Landmarks Detection in?Mitral Regurgitationis a complex minimally invasive procedure which is facing the problem of data availability and data privacy. Therefore, the simulation cases are widely used to form surgery training and planning. However, the cross-domain gap may affect the performance significantly as Deep Learning methods rely hea作者: angiography 時(shí)間: 2025-3-23 19:56 作者: 胖人手藝好 時(shí)間: 2025-3-23 22:57
Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentthods heavily rely on the volume of labeled data. However, manually annotating location of tools in surgical videos is quite time-consuming. To overcome this problem, we propose a semi-supervised pipeline for surgical tool detection, using strategies of highly confident pseudo labeling and strong au作者: 夾克怕包裹 時(shí)間: 2025-3-24 06:11
One-Shot Learning for Landmarks Detectionlems but it usually requires a large number of the annotated datasets for the training stage. In addition, traditional methods usually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetric images from a single example ba作者: Fsh238 時(shí)間: 2025-3-24 09:23 作者: Circumscribe 時(shí)間: 2025-3-24 11:12
Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmentation and domain adaptation during training improves the performance of the resulting deep learning models, even when tested within the observed domain.作者: falsehood 時(shí)間: 2025-3-24 15:20
https://doi.org/10.1007/978-3-662-43839-8e variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.作者: Baffle 時(shí)間: 2025-3-24 21:28 作者: Serenity 時(shí)間: 2025-3-25 02:20
https://doi.org/10.1007/978-3-662-43839-8y distributions within each masked region using a novel Variational Autoencoder (VAE) based hierarchical probabilistic network. Our approach then generates a diverse set of inpainted images, all of which appear visually appropriate.作者: 拋物線 時(shí)間: 2025-3-25 05:03 作者: nutrients 時(shí)間: 2025-3-25 09:07
Conditional Generation of Medical Images via Disentangled Adversarial Inferencee variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.作者: 灌溉 時(shí)間: 2025-3-25 13:12 作者: 怒目而視 時(shí)間: 2025-3-25 16:52 作者: crescendo 時(shí)間: 2025-3-25 21:32
One-Shot Learning for Landmarks Detectionthm in order to perform automatic organ localization and landmark matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The results show that our one-shot learning scheme converges well and leads to a good accuracy of the landmark positions.作者: reperfusion 時(shí)間: 2025-3-26 02:02 作者: Arrhythmia 時(shí)間: 2025-3-26 06:03
Conception of Design Science and its Methods latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmentation and domain adaptation during training improves the performance of the resulting deep learning models, even when tested within the observed domain.作者: CHOIR 時(shí)間: 2025-3-26 12:20
Helena M. Müller,Melanie Reuter-Oppermanndel is trained to generate fake brain connectivity matrices, which are expected to reflect the latent distribution and topological features of the real brain network data. Numerical results show that the BrainNetGAN outperforms the benchmarking algorithms in augmenting the brain networks for AD classification tasks.作者: Ingenuity 時(shí)間: 2025-3-26 14:58 作者: Allergic 時(shí)間: 2025-3-26 19:44 作者: 連鎖,連串 時(shí)間: 2025-3-26 22:36
Sandeep Purao,Arvind Karunakaran was conducted to validate the effectiveness of the two strategies in our tool detection pipeline, and the results show the mAP improvement of 1.9% and 3.9%, respectively. The proposed dataset, CaDTD, is publicly available at ..作者: GRIPE 時(shí)間: 2025-3-27 02:44
BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementdel is trained to generate fake brain connectivity matrices, which are expected to reflect the latent distribution and topological features of the real brain network data. Numerical results show that the BrainNetGAN outperforms the benchmarking algorithms in augmenting the brain networks for AD classification tasks.作者: ensemble 時(shí)間: 2025-3-27 05:25
Evaluating GANs in Medical ImagingNN) trained as a discriminator. On the other hand, we compute domain-independent metrics catching the image high-level quality. We also introduce a visual layer explaining the CNN. We extensively evaluate the proposed approach with 4 state-of-the-art GANs over a real-world medical dataset of CT lung images.作者: Cupping 時(shí)間: 2025-3-27 12:03
Scalable Semi-supervised Landmark Localization for X-ray Images Using Few-Shot Deep Adaptive Graphpose another loss using Jensen–Shannon (JS) divergence to regulate the consistency of the intermediate feature maps. We extensively evaluated our method on pelvis, hand and chest landmark detection tasks. Our experiment results demonstrate consistent and significant improvements over previous methods.作者: 粘連 時(shí)間: 2025-3-27 15:26 作者: 擁擠前 時(shí)間: 2025-3-27 20:48
Actuator Principles and Classification radiotherapy planning when only an MR image is available. Recent approaches have made large strides in solving this challenging synthesis problem with convolutional neural networks that learn a mapping from MR inputs to CT outputs. In this paper, we find that all existing approaches share a common 作者: Traumatic-Grief 時(shí)間: 2025-3-27 22:39
Conception of Design Science and its Methodsent imaging settings. We address these problems using a novel variational style-transfer neural network that can sample various styles from a computed latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmenta作者: CLAMP 時(shí)間: 2025-3-28 06:05
Constituent Areas of Design Scienceedical applications, such as, image enhancement and disease progression modeling. Current GAN technologies for 3D medical image synthesis must be significantly improved to be suitable for real-world medical problems. In this paper, we extend the state-of-the-art StyleGAN2 model, which natively works作者: 表示向下 時(shí)間: 2025-3-28 08:55
https://doi.org/10.1007/978-1-4471-3091-8on burden for developing machine learning methods. GANs have been used successfully to translate images from one domain to another, such as MR to CT. At present, paired data (registered MR and CT images) or extra supervision (e.g. segmentation masks) is needed to learn good translation models. Regis作者: 豐滿中國 時(shí)間: 2025-3-28 13:14 作者: hidebound 時(shí)間: 2025-3-28 15:41 作者: 停止償付 時(shí)間: 2025-3-28 22:47
https://doi.org/10.1007/978-3-662-43839-8combined with other tools to remove artifacts or fill in occluded regions, allowing better understanding of the images from doctors or downstream algorithms. However, current methods that solve the problem usually pay no attention to the underlying pixel-intensity distributions in the missing input 作者: 品嘗你的人 時(shí)間: 2025-3-29 02:59
David M. Lehmann,Viktor M. Saleniusons (classification), as biomedical images naturally contain image-wise labels in many scenarios. The current weakly supervised learning algorithms from the computer vision community are largely designed for focal objects (e.g., dogs and cats). However, such algorithms are not optimized for diffuse 作者: anagen 時(shí)間: 2025-3-29 03:30 作者: 一起平行 時(shí)間: 2025-3-29 07:15
Helena M. Müller,Melanie Reuter-Oppermannan open issue. This is especially true in medical imaging where GAN application is at its infancy, and where the use of scores based on models trained on datasets far away from the medical domain, e.g. the Inception score, can lead to misleading results. To overcome such limitations we propose a fra作者: FLIC 時(shí)間: 2025-3-29 14:31 作者: CHAFE 時(shí)間: 2025-3-29 17:58
https://doi.org/10.1007/978-3-031-61175-9is a complex minimally invasive procedure which is facing the problem of data availability and data privacy. Therefore, the simulation cases are widely used to form surgery training and planning. However, the cross-domain gap may affect the performance significantly as Deep Learning methods rely hea作者: Silent-Ischemia 時(shí)間: 2025-3-29 21:14 作者: 不可接觸 時(shí)間: 2025-3-30 01:30 作者: Concomitant 時(shí)間: 2025-3-30 05:14
Sandeep Purao,Arvind Karunakaranlems but it usually requires a large number of the annotated datasets for the training stage. In addition, traditional methods usually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetric images from a single example ba作者: Introduction 時(shí)間: 2025-3-30 08:31 作者: guzzle 時(shí)間: 2025-3-30 14:17
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/264556.jpg作者: 頌揚(yáng)本人 時(shí)間: 2025-3-30 19:18
