mednist_tutorial(DenseNet121):This notebook shows how to easily integrate MONAI features into existing PyTorch programs. It's based on theMedNISTdataset which is very suitable for beginners as a tutorial. This tutorial also makes use of MONAI's in-built occlusion sensitivity functionality. 主要步...
networks Removed hard-coded spatial_dims in SwinTransformer (#7302) Fixed learnable position_embeddings in PatchEmbeddingBlock (#7564, #7605) Removed memory_pool_limit in TRT config (#7647) Propagated kernel_size to ConvBlocks within AttentionUnet (#7734) Addressed hard-coded activation layer in ...
It also uses network modules, such asConvolution, and the layer factory to easily handle 2D or 3D inputs using the same module interface. The loss and metrics modules make the model training and evaluation simple. This implementation also includes a working example of training and validation pip...
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/nets/unet.py The default Unet module now only uses deconvolution for feature map upsampling. It introduces checkerboard artefacts when you use it as a GAN generator. Can w...
Self-attention-based network blocks now support both 2D and 3D inputsRemovedThe deprecated TransformInverter, in favor of monai.transforms.InvertD GitHub self-hosted CI/CD pipelines for nightly and post-merge tests monai.handlers.utils.evenly_divisible_all_gather monai.handlers.utils.string_list_...