找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

[復(fù)制鏈接]
樓主: Lactase
11#
發(fā)表于 2025-3-23 09:49:36 | 只看該作者
12#
發(fā)表于 2025-3-23 14:06:43 | 只看該作者
SVoRT: Iterative Transformer for?Slice-to-Volume Registration in?Fetal Brain MRIing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
13#
發(fā)表于 2025-3-23 20:49:12 | 只看該作者
Double-Uncertainty Guided Spatial and?Temporal Consistency Regularization Weighting for?Learning-Basistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance unc
14#
發(fā)表于 2025-3-24 01:30:08 | 只看該作者
On the?Dataset Quality Control for?Image Registration Evaluationtasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improv
15#
發(fā)表于 2025-3-24 05:07:57 | 只看該作者
Dual-Branch Squeeze-Fusion-Excitation Module for?Cross-Modality Registration of?Cardiac SPECT and?CTinvestigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived .-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be
16#
發(fā)表于 2025-3-24 09:58:25 | 只看該作者
17#
發(fā)表于 2025-3-24 11:46:08 | 只看該作者
Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning
18#
發(fā)表于 2025-3-24 17:38:41 | 只看該作者
19#
發(fā)表于 2025-3-24 19:48:28 | 只看該作者
20#
發(fā)表于 2025-3-25 00:01:00 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-11-2 13:20
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
苍溪县| 壶关县| 恩平市| 正阳县| 舟曲县| 郓城县| 措美县| 聂拉木县| 井冈山市| 衡阳市| 广安市| 丹凤县| 永胜县| 清徐县| 肃宁县| 大同市| 绥阳县| 安阳县| 巴东县| 旌德县| 阿勒泰市| 潮州市| 瑞昌市| 永兴县| 海林市| 塔河县| 南郑县| 萍乡市| 金溪县| 卢氏县| 朝阳区| 盐边县| 金乡县| 平和县| 揭东县| 六枝特区| 安泽县| 阜新| 温州市| 天津市| 屏边|