標(biāo)題: Titlebook: Biomedical Image Registration; 11th International W Marc Modat,Ivor Simpson,Tony C. W. Mok Conference proceedings 2024 The Editor(s) (if ap [打印本頁(yè)] 作者: 候選人名單 時(shí)間: 2025-3-21 20:07
書(shū)目名稱Biomedical Image Registration影響因子(影響力)
作者: 閑蕩 時(shí)間: 2025-3-21 20:44
https://doi.org/10.1007/978-90-481-9679-1ccuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration作者: 表皮 時(shí)間: 2025-3-22 03:23
https://doi.org/10.1007/978-90-481-9679-1red 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the al作者: 時(shí)間等 時(shí)間: 2025-3-22 07:17
Valentina Cuccio,Mario Grazianos employ multi-step network structures to break the large deformation into smaller ones and register them separately. However, these methods have two problems. First, they cannot effectively discriminate between hard-to-optimize large deformations and easy-to-optimize small deformations. This indist作者: 流利圓滑 時(shí)間: 2025-3-22 12:03 作者: 哭得清醒了 時(shí)間: 2025-3-22 16:48 作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-22 17:10 作者: concubine 時(shí)間: 2025-3-22 23:13
Valentina Cuccio,Mario Grazianod this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical q作者: cacophony 時(shí)間: 2025-3-23 03:05
Neural Networks, Human Time, and the Soul,cial to assess model robustness under such shifts, often accomplished using simulated domain shifts and expert annotations, e.g., landmarks. This work presents ProactiV-Reg, an annotation-free approach that utilizes a learnable image mapping: it iteratively adjusts a moving image to align with a fix作者: 強(qiáng)壯 時(shí)間: 2025-3-23 07:32
https://doi.org/10.1057/9781403937582pends on the reliability of the used similarity metric. In this work, we systematically challenge the robustness of two such popular metrics, Mutual Information and Cross-Cumulative Residual Entropy, by employing adversarial techniques from the deep learning field. Our experiments show resistance to作者: BILE 時(shí)間: 2025-3-23 10:38
Metaphors of International Security,on coefficient (ADC) maps which can supplement differentiation between malignant and benign breast lesions. However, artifacts in DWI are not infrequent, e.g., due to patient motion, pulsation or other sources, which can cause shifts between the different b-value acquisitions and affect the accuracy作者: COUCH 時(shí)間: 2025-3-23 15:35
Metaphors in International Relations Theoryhave demonstrated reasonable accuracy, they can produce abnormal deformations that introduce substantial artifacts in medical images by unrealistically modifying the shape and position of anatomical structures. These abnormal deformations may not be effectively detected or mitigated during inference作者: 青春期 時(shí)間: 2025-3-23 21:09 作者: Phagocytes 時(shí)間: 2025-3-24 00:46 作者: Orgasm 時(shí)間: 2025-3-24 04:39 作者: 思想 時(shí)間: 2025-3-24 07:43 作者: ellagic-acid 時(shí)間: 2025-3-24 10:43
Ronald S. Valle,Rolf Eckartsbergltiple subjects. Machine learning registration methods have achieved excellent speed and accuracy but lack interpretability and flexibility at test time (since their deformation model is fixed). More recently, keypoint-based methods have been proposed to tackle these issues, but their accuracy is st作者: 縮影 時(shí)間: 2025-3-24 17:21 作者: 一致性 時(shí)間: 2025-3-24 19:06
https://doi.org/10.1007/978-3-031-73480-9Image registration; Linear and nonlinear spatial transformation; Sliding motion; Multi-channel and grou作者: 極大的痛苦 時(shí)間: 2025-3-25 01:05
978-3-031-73479-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Cultivate 時(shí)間: 2025-3-25 04:46
MultiGradICON: A Foundation Model for?Multimodal Medical Image Registrationccuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration作者: 主動(dòng) 時(shí)間: 2025-3-25 09:50
XSynthMorph: Generative-Guided Deformation for?Unsupervised Ill-Posed Volumetric Recoveryred 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the al作者: 痛打 時(shí)間: 2025-3-25 11:41 作者: 配置 時(shí)間: 2025-3-25 17:03
Unleashing Registration: Diffusion Models for?Synthetic Paired 3D Training DataFor the task of image registration, this is especially relevant, since networks rely on image pairs for training. Augmenting the dataset with synthetic deformations does not suffice for anonymous publication, as patient-specific topologies are kept. To handle this, we propose to leverage vector quan作者: 放棄 時(shí)間: 2025-3-25 21:44
Feedback Attention for?Unsupervised Cardiac Motion Estimation in?3D Echocardiographyliable performance with deep learning image registration (DLIR) is traditionally challenging due to intrinsic noise and fuzzy anatomic boundaries in echocardiography. It is further advantageous to achieve DLIR in 3D, as the cardiac anatomy has complex 3D structures and motions that are difficult to 作者: Alveoli 時(shí)間: 2025-3-26 00:26 作者: UNT 時(shí)間: 2025-3-26 06:04
Mamba? Catch The Hype Or Rethink What Really Helps for?Image Registrationd this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical q作者: hemoglobin 時(shí)間: 2025-3-26 12:32
Assessing the?Robustness of?Image Registration Models Under Domain Shifts with?Learnable Input Imagecial to assess model robustness under such shifts, often accomplished using simulated domain shifts and expert annotations, e.g., landmarks. This work presents ProactiV-Reg, an annotation-free approach that utilizes a learnable image mapping: it iteratively adjusts a moving image to align with a fix作者: 浮雕 時(shí)間: 2025-3-26 13:15 作者: 凝結(jié)劑 時(shí)間: 2025-3-26 17:47
Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mon coefficient (ADC) maps which can supplement differentiation between malignant and benign breast lesions. However, artifacts in DWI are not infrequent, e.g., due to patient motion, pulsation or other sources, which can cause shifts between the different b-value acquisitions and affect the accuracy作者: 善變 時(shí)間: 2025-3-26 22:47
A Learning-Free Approach to?Mitigate Abnormal Deformations in?Medical Image Registrationhave demonstrated reasonable accuracy, they can produce abnormal deformations that introduce substantial artifacts in medical images by unrealistically modifying the shape and position of anatomical structures. These abnormal deformations may not be effectively detected or mitigated during inference作者: Antagonist 時(shí)間: 2025-3-27 01:44 作者: Instrumental 時(shí)間: 2025-3-27 07:36 作者: 姑姑在炫耀 時(shí)間: 2025-3-27 09:53 作者: MOT 時(shí)間: 2025-3-27 16:44 作者: 射手座 時(shí)間: 2025-3-27 18:00 作者: 厚臉皮 時(shí)間: 2025-3-27 23:55
Large Deformation Registration with?A Confidence-Guided Networkattention registration part to register the image with the proper receptive field. Comprehensive experimental results on the brain and liver datasets show that our proposed confidence-guided network significantly improves registration accuracy over existing methods for large deformation registration作者: 符合你規(guī)定 時(shí)間: 2025-3-28 03:01
Feedback Attention for?Unsupervised Cardiac Motion Estimation in?3D Echocardiographyrate its good performance in both adult and fetal echocardiography images. We introduce a novel feedback spatial transformer module where the registration outputs are used to generate a co-attention map that describes the remaining registration errors to guide the network’s spatial emphasis during D作者: Chagrin 時(shí)間: 2025-3-28 07:07
Learning Deformable Intra-Patient Liver Registration with?Graph Cross-Attentions-attention. Our proposed module is a novel solution that can enhance the performance of various encoder-decoder architectures. To the best of our knowledge, this is the first application of a graph cross-attention mechanism for liver registration. We carried out the experimental validation on 20 pr作者: neolith 時(shí)間: 2025-3-28 13:16 作者: 使困惑 時(shí)間: 2025-3-28 14:36 作者: mitten 時(shí)間: 2025-3-28 20:58
A Learning-Free Approach to?Mitigate Abnormal Deformations in?Medical Image Registrationh the input images of the registration model and the model weights at inference, making it adaptable to a wide range of deep-learning-based medical image registration models. Next, the proposed approach uses the variabilities in the estimated registration deformation fields to mitigate significant d作者: 悶熱 時(shí)間: 2025-3-29 02:13
Deformable MRI Sequence Registration for?AI-Based Prostate Cancer Diagnosiss. Rigid registration showed no improvement. Deformable registration demonstrated a substantial improvement in lesion overlap (+10% median Dice score) and a positive yet non-significant improvement in diagnostic performance (+0.3% AUROC, p?=?0.18). Our investigation shows that a substantial improvem作者: SHRIK 時(shí)間: 2025-3-29 05:42 作者: buoyant 時(shí)間: 2025-3-29 10:34
Deep Learning Multi-channel Structural and?Diffusion Tensor Neonatal Image Registration common atlas space. We apply the technique to align multi-channel datasets composed of structural .-weighted (.w) MRI and DTI maps into atlas space. The quantitative and qualitative evaluation confirmed that when we use the two modalities we obtain good alignment of anatomical structures, while als作者: 兇猛 時(shí)間: 2025-3-29 15:19 作者: 伸展 時(shí)間: 2025-3-29 18:25
Valentina Cuccio,Mario Grazianoattention registration part to register the image with the proper receptive field. Comprehensive experimental results on the brain and liver datasets show that our proposed confidence-guided network significantly improves registration accuracy over existing methods for large deformation registration作者: Robust 時(shí)間: 2025-3-29 23:12 作者: Pudendal-Nerve 時(shí)間: 2025-3-30 00:24 作者: Entirety 時(shí)間: 2025-3-30 06:19 作者: 搜集 時(shí)間: 2025-3-30 11:12
Metaphors of International Security,ring mean, standard deviation, and within-lesion coefficient of variance (CoV), using repeated-measures ANOVA alongside Dunnett post-hoc tests in manual segmentations of the target lesions performed on ultra-high b-value images. In addition, we evaluated between-patient CoV and area under the curve 作者: cauda-equina 時(shí)間: 2025-3-30 14:42 作者: Palter 時(shí)間: 2025-3-30 17:05 作者: LAY 時(shí)間: 2025-3-30 21:55 作者: manifestation 時(shí)間: 2025-3-31 03:22 作者: 搖晃 時(shí)間: 2025-3-31 06:55 作者: landfill 時(shí)間: 2025-3-31 10:17 作者: STENT 時(shí)間: 2025-3-31 17:12
The Metaphorical Role of the Histone Codein the form of 3D vessel representations of intra-patient lung scan pairs by volumetric rasterisation. With paired synthetic data, only minimal fine-tuning on privacy-sensitive real data is needed to achieve comparable or even better results to training on a large real dataset. The code is publicly available at ..作者: 有惡意 時(shí)間: 2025-3-31 19:11 作者: obscurity 時(shí)間: 2025-3-31 22:05
Unleashing Registration: Diffusion Models for?Synthetic Paired 3D Training Datain the form of 3D vessel representations of intra-patient lung scan pairs by volumetric rasterisation. With paired synthetic data, only minimal fine-tuning on privacy-sensitive real data is needed to achieve comparable or even better results to training on a large real dataset. The code is publicly available at ..
