作者: Harridan 時間: 2025-3-21 21:27
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Networkf segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the mo作者: 凹室 時間: 2025-3-22 04:20
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patientse appearance changes. This paper describes our contribution to the registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends our previously developed registration 作者: Isolate 時間: 2025-3-22 05:34
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumoences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based 作者: 膽汁 時間: 2025-3-22 09:56 作者: apiary 時間: 2025-3-22 14:15
Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modalityctures. The Koos classification captures many of the characteristics of treatment decisions and is often used to determine treatment plans. Although both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2) scanning can be used for Koos Classification, hrT2 scanning is gaining interest作者: amygdala 時間: 2025-3-22 20:56
MS-MT: Multi-scale Mean Teacher with?Contrastive Unpaired Translation for?Cross-Modality Vestibular sing cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures?. Vestibular Schwannoma (VS) and C作者: shrill 時間: 2025-3-22 22:50 作者: LAVA 時間: 2025-3-23 04:22
Weakly Unsupervised Domain Adaptation for?Vestibular Schwannoma Segmentationare contrast-enhanced T1 (ceT1), with a growing interest in high-resolution T2 images (hrT2) to replace ceT1, which involves the use of a contrast agent. As hrT2 images are currently scarce, it is less likely to train robust machine learning models to segment VS or other brain structures. In this wo作者: 玉米棒子 時間: 2025-3-23 06:21
Multi-view Cross-Modality MR Image Translation for?Vestibular Schwannoma and?Cochlea Segmentation MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hr. images reflecting different perspecti作者: 沒血色 時間: 2025-3-23 10:41
Enhancing Data Diversity for?Self-training Based Unsupervised Cross-Modality Vestibular Schwannoma agmentation methods have shown promising results without requiring the time-consuming and laborious manual labeling process. In this paper, we present an approach for VS and cochlea segmentation in an unsupervised domain adaptation setting. Specifically, we first develop a cross-site cross-modality u作者: 腐蝕 時間: 2025-3-23 16:54
Regularized Weight Aggregation in?Networked Federated Learning for?Glioblastoma Segmentationoration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggr作者: GLIB 時間: 2025-3-23 22:02
A Local Score Strategy for?Weight Aggregation in?Federated Learningieve a competitive result like in centralised settings. FeTS Challenge is an initiative focusing on federated learning and robustness to distribution shifts between medical institutions for brain tumor segmentation. In this paper, we describe a method based on the local score rate for the weight agg作者: Consequence 時間: 2025-3-23 23:28 作者: champaign 時間: 2025-3-24 04:22 作者: certain 時間: 2025-3-24 10:04 作者: 辯論 時間: 2025-3-24 13:24 作者: Inflamed 時間: 2025-3-24 14:54
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma PatientsE of 2.93?±?1.63?mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the deformable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The dock作者: folliculitis 時間: 2025-3-24 19:53
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumos composed of a standard image similarity measure, a diffusion regularizer, and an edge-map similarity measure added to overcome intensity dependence and reinforce correct boundary deformation. We observed that the addition of the Inception module substantially increased the performance of the netwo作者: hangdog 時間: 2025-3-24 23:32
Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modalityty domain adaptation method based on image translation by transforming annotated ceT1 scans into hrT2 modality and using their annotations to achieve supervised learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related to Koos classification in hrT2 scans were segmented. Finall作者: Conspiracy 時間: 2025-3-25 07:24
MS-MT: Multi-scale Mean Teacher with?Contrastive Unpaired Translation for?Cross-Modality Vestibular l scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of . and . and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the cros作者: Encapsulate 時間: 2025-3-25 09:09 作者: Tartar 時間: 2025-3-25 14:23 作者: 法律的瑕疵 時間: 2025-3-25 15:53 作者: 勉勵 時間: 2025-3-25 23:47
Efficient Federated Tumor Segmentation via?Parameter Distance Weighted Aggregation and?Client Prunineneity, largely affecting the training behavior. The heterogeneous data results in the variation of clients’ local optimization, therefore making the local client update not consistent with each other. The vanilla weighted average aggregation only takes the number of samples into account but ignores作者: 清澈 時間: 2025-3-26 04:10
Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries8th International Wo作者: 客觀 時間: 2025-3-26 08:00 作者: abolish 時間: 2025-3-26 11:54
P. Cerletti,F. Bonomi,S. Paganiollow-up images of 4 different modalities including t1, t1ce, flair and t2 are provided. For each case, we apply QPDIR to register image pairs of each modality to produce the deformation field, and then add the deformation field to landmarks, and merge the predict landmarks of each modality together作者: Antigen 時間: 2025-3-26 15:21 作者: 移植 時間: 2025-3-26 18:46 作者: 休閑 時間: 2025-3-26 23:41
Molar Masses and Molar Mass Distributionsty domain adaptation method based on image translation by transforming annotated ceT1 scans into hrT2 modality and using their annotations to achieve supervised learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related to Koos classification in hrT2 scans were segmented. Finall作者: 揉雜 時間: 2025-3-27 01:41
Polymer crystallization theories,l scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of . and . and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the cros作者: NOMAD 時間: 2025-3-27 06:49
Timothy A. Springer,Jay C. Unkelesse data augmentation, we use CUT and CycleGAN to generate two groups of realistic T2 volumes with different details and appearances for supervised segmentation training. For online data augmentation, we design a random tumor signal reducing method for simulating the heterogeneity of VS tumor signals.作者: confide 時間: 2025-3-27 10:33 作者: harbinger 時間: 2025-3-27 17:27
Venkata Sita Rama Raju Allam,Maria B. Sukkar 2021 competition amongst over 1200 excellent researchers from all over the world, and robustly produced outstanding segmentation results across different unseen datasets from various institutions in the FeTS 2022 Challenge, which achieved Dice score of 0.9256, 0.8774, 0.8576 and Hausdorff Distances作者: 確定無疑 時間: 2025-3-27 18:07
Liposomal Delivery for Targeting Macrophageseneity, largely affecting the training behavior. The heterogeneous data results in the variation of clients’ local optimization, therefore making the local client update not consistent with each other. The vanilla weighted average aggregation only takes the number of samples into account but ignores作者: Cryptic 時間: 2025-3-28 00:24 作者: 討好美人 時間: 2025-3-28 04:46 作者: Efflorescent 時間: 2025-3-28 08:43
https://doi.org/10.1007/978-3-031-44153-0Brain; Glioma; Glioblastoma; Brain lesion; Segmentation; Multiple sclerosis; Traumatic brain injury; CAD; Ma作者: ARK 時間: 2025-3-28 10:29
978-3-031-44152-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Iatrogenic 時間: 2025-3-28 15:41 作者: 留戀 時間: 2025-3-28 20:24
P. Cerletti,F. Bonomi,S. Paganif segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the mo作者: metropolitan 時間: 2025-3-28 23:03
Molar Masses and Molar Mass Distributionse appearance changes. This paper describes our contribution to the registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends our previously developed registration 作者: 切掉 時間: 2025-3-29 03:28 作者: CROAK 時間: 2025-3-29 10:33
https://doi.org/10.1007/978-1-4615-7367-8In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, 作者: CAJ 時間: 2025-3-29 13:09 作者: Ganglion 時間: 2025-3-29 15:43
Polymer crystallization theories,sing cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures?. Vestibular Schwannoma (VS) and C作者: 朝圣者 時間: 2025-3-29 23:21
Timothy A. Springer,Jay C. Unkelessy leveraging labeled contrast-enhanced T1 scans. The 2022 edition extends the segmentation task by including multi-institutional scans. In this work, we proposed an unpaired cross-modality segmentation framework using data augmentation and hybrid convolutional networks. Considering heterogeneous dis作者: 遺傳 時間: 2025-3-30 00:38
Dolph O. Adams,Michael G. Hannaare contrast-enhanced T1 (ceT1), with a growing interest in high-resolution T2 images (hrT2) to replace ceT1, which involves the use of a contrast agent. As hrT2 images are currently scarce, it is less likely to train robust machine learning models to segment VS or other brain structures. In this wo作者: 焦慮 時間: 2025-3-30 05:06 作者: obviate 時間: 2025-3-30 11:32
Macrophages in the Uterus and Placenta,gmentation methods have shown promising results without requiring the time-consuming and laborious manual labeling process. In this paper, we present an approach for VS and cochlea segmentation in an unsupervised domain adaptation setting. Specifically, we first develop a cross-site cross-modality u作者: 一致性 時間: 2025-3-30 12:51
https://doi.org/10.1007/978-3-642-77377-8oration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggr作者: 頭盔 時間: 2025-3-30 17:45 作者: debouch 時間: 2025-3-30 21:55
Venkata Sita Rama Raju Allam,Maria B. Sukkaron models have been proposed. Based on the platform that BraTS challenge 2021 provided for researchers, we implemented a battery of cutting-edge deep neural networks, such as nnU-Net, UNet++, CoTr, HRNet, and Swin-Unet to compare performances amongst distinct models directly. To improve segmentation作者: Indecisive 時間: 2025-3-31 03:46
