swin-transformer讲解:https://www.bilibili.com/video/BV1pL4y1v7jCswin-unetr: https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21, 视频播放量 3463、弹幕量 0、点赞数 16、投硬币枚数 8、收藏人数 70、转发人数 7, 视频作者 青梅
conda create -n 新环境名 --clone旧环境名 这里我们的环境名就叫 swinunetr ,这样就不会乱啦~~~ 一切都准备就绪之后,我们就去看官网啦,Swin UNETR 有 BTCV (CT)和 BTS2021 (MRI)两种,这里我用的是 BTCV ,因为我自己的数据集是 CT 的。 因为官网里面没有告诉我们 dataset 的架构,所以我用的还是之前 ...
受视觉转换器及其变体成功的启发,我们提出了一种新颖的分割模型,称为 Swin UNET TRansformers (Swin UNETR)。具体来说,将 3D 脑肿瘤语义分割任务重新表述为序列到序列预测问题,其中多模态输入数据被投影到 1D 嵌入序列中,并用作分层 Swin 变换器的输入作为编码器。 swin 转换器编码器通过利用移位窗口计算自注意力...
本篇文章和上一篇Swin-Unet类似,利用Transformer 提出了用于brain tumor的分割方法 -Swin UNETR。 Method 网络结构和U-Net 类似,主要使用的是Swin Transformer Block和Swin-Unet中的编码器类似,只不过输入数据是3D 的MR 图像。需要注意的是Swin Transformer 中的 W-MSA和SW-MSA均在3维图像上计算,如下图所示。
Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input ...
nohup tensorboard --port 6007 --logdir /root/autodl-tmp/SwinUNETR/runs& cache_rate是用来控制缓存数据的。如果被kill了,就加上--use_normal_dataset num_samples就当batch_siz --workers 0不要删。 val_every是多少epoch validation一次。每次validation都会保存一次模型 ...
[MICCAI 2023] Continual Learning for Abdominal Multi-Organ and Tumor Segmentation - ContinualLearning/model/swinunetr.py at main · gkw0010/ContinualLearning
We employ CPS to enhance the state-of-the-art SwinUNETR model for medical image segmentation, initially pre-trained on the BraTs2021 dataset, this enhanced model is subsequently applied to three hippocampal datasets. The results reveal that CPS significantly outperforms existing methods, increasing ...
The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and ...
Objective: To distinguish infarct location and type with the utmost precision using the advantages of the Swin UNEt TRansformers (Swin UNETR) architecture. Methods: The research employed a two-phase training approach. In the first phase, the Swin UNETR model was trained using the Isc...