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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

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樓主: Traction
51#
發(fā)表于 2025-3-30 09:12:46 | 只看該作者
An Explainable Deep Framework: Towards Task-Specific Fusion for?Multi-to-One MRI Synthesisvarious reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple available sequences for missing sequence synthesis. Despite their success, these methods lack the ability to quantify the contributio
52#
發(fā)表于 2025-3-30 13:23:14 | 只看該作者
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortu
53#
發(fā)表于 2025-3-30 17:32:21 | 只看該作者
Alias-Free Co-modulated Network for?Cross-Modality Synthesis and?Super-Resolution of?MR Imagesed modality images and reduce slice thickness for magnetic resonance imaging (MRI), respectively. It is also desirable to build a network for simultaneous cross-modality and super-resolution (CMSR) so as to further bridge the gap between clinical scenarios and research studies. However, these works
54#
發(fā)表于 2025-3-30 23:48:20 | 只看該作者
Multi-perspective Adaptive Iteration Network for?Metal Artifact Reductionality of metal-corrupted image remains a challenge. Although the deep learning-based MAR methods have achieved impressive success, their interpretability and generalizability need further improvement. It is found that metal artifacts mainly concentrate in high frequency, and their distributions in t
55#
發(fā)表于 2025-3-31 01:32:56 | 只看該作者
56#
發(fā)表于 2025-3-31 07:07:09 | 只看該作者
Low-Dose CT Image Super-Resolution Network with?Dual-Guidance Feature Distillation and?Dual-Path Cons have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted; (2) features with fixed-resolution lose the attention to multi-scale information; (3) single super-resolution module fails to balance detai
57#
發(fā)表于 2025-3-31 10:34:52 | 只看該作者
58#
發(fā)表于 2025-3-31 14:19:35 | 只看該作者
59#
發(fā)表于 2025-3-31 20:44:53 | 只看該作者
60#
發(fā)表于 2025-3-31 23:07:31 | 只看該作者
Feature-Conditioned Cascaded Video Diffusion Models for?Precise Echocardiogram Synthesisobustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by provi
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