拿着锡 发表于 2025-3-21 16:37:20
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978-3-031-47424-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl泥瓦匠 发表于 2025-3-22 02:37:37
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops978-3-031-47425-5Series ISSN 0302-9743 Series E-ISSN 1611-3349Pathogen 发表于 2025-3-22 06:52:31
https://doi.org/10.1007/978-3-031-47425-5Artificial Intelligence; Computer Vision; Machine Learning; Medical Imaging; Explainability; Privacy-Pres加剧 发表于 2025-3-22 11:37:56
http://reply.papertrans.cn/63/6293/629229/629229_5.pnghieroglyphic 发表于 2025-3-22 13:47:24
FusionNet: A Frame Interpolation Network for 4D Heart Modelsperimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: ..甜食 发表于 2025-3-22 18:17:48
Graph-Based Multimodal Multi-lesion DLBCL Treatment Response Prediction from PET Imagesly. The model is trained and evaluated on a private prospective multicentric dataset of 583 patients. Experimental results show that our proposed method outperforms classical supervised methods based on either clinical, imaging or both clinical and imaging data for the 2-year progression-free survival (PFS) classification accuracy.Mindfulness 发表于 2025-3-22 22:03:46
0302-9743Image Computing and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC, Canada, in October 2023. .The 54 full papers together with 14 short papers presented in this volume were carefully reviewed and selected from 123 submissions from all workshops...The papersAutobiography 发表于 2025-3-23 02:02:18
Conference proceedings 2023puting and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC, Canada, in October 2023. .The 54 full papers together with 14 short papers presented in this volume were carefully reviewed and selected from 123 submissions from all workshops...The papers of the woFLORA 发表于 2025-3-23 06:26:04
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transforre named Reconstruction Swin Transformer (RST) for 4D MRI. RST inherits the backbone design of the Video Swin Transformer with a novel reconstruction head introduced to restore pixel-wise intensity. A convolution network called SADXNet is used for rapid initialization of 2D MR frames before RST lear