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  • Extended nnU-Net for Brain Metastasis Detection and . . . - PubMed
    Results: Of the 6 nnU-Net systems, the nnU-Net with adaptive Dice loss achieved the best detection and segmentation performance on the first testing data set At an FP rate of 0 65 ± 1 17, overall sensitivity was 0 904 for all sizes of BM, 0 966 for BM ≥0 1 cm 3, and 0 824 for BM <0 1 cm 3
  • Extended nnU-Net for Brain Metastasis Detection and Segmentation in . . .
    In this study, we aimed to extend the nnU-Net framework for detecting and contouring BMs from 3D post-Gd MRI scans with particular efforts to enhance the detection of small brain metastatic lesions while reducing false positives
  • rylezhou nnUNet-pytorch - GitHub
    nnU-Net is the first segmentation method that is designed to deal with the dataset diversity found in the domain It condenses and automates the keys decisions for designing a successful segmentation pipeline for any given dataset nnU-Net makes the following contributions to the field:
  • 论文创新大杀器！LSTM结合UNet，性能突出，涨点显著_xlstm-unet-CSDN博客
    最近，浙大等团队提出了xLSTM- UNet，通过将U-Mamba中的Mamba换成xLSTM，就可以直接提升2D和3D医学图像分割性能，涨点效果显著！ xLSTM-UNet是一种 结合了LSTM和UNet 的混合 网络模型，这类模型保留了UNet出色的空间特征提取能力，增加了对时序信息的捕捉和处理能力，能显著提高分割的精度和准确性。 因此，它被广泛应用于 医学图像分割 、遥感图像处理、工业检测等领域，同时也 为复杂场景下的图像处理任务提供了新的思路和创新方向。 我这次整理了 8篇LSTM结合UNet的最新paper，给苦于创新点的同学作参考，希望各位看完后可以有些启发，更快写出论文中稿。 论文原文+开源代码需要的同学看文末
  • tianrun-chen xLSTM-UNet-PyTorch - GitHub
    U-xLSTM is built on the popular nnU-Net framework Our data processing approach strictly follows the methods outlined in the U-Mamba study This includes steps such as data normalization, augmentation techniques, and segmentation algorithms detailed in their publication {dataset_type}: Specifies the type of dataset to be used
  • MIC-DKFZ nnUNet - GitHub
    nnU-Net is a semantic segmentation method that automatically adapts to a given dataset It will analyze the provided training cases and automatically configure a matching U-Net-based segmentation pipeline No expertise required on your end! You can simply train the models and use them for your application
  • 论文创新大杀器！LSTM结合UNet，性能突出，涨点显著 - 知乎
    最近，浙大等团队提出了xLSTM-UNet，通过将 U-Mamba 中的Mamba换成xLSTM，就可以直接提升2D和3D医学图像分割性能，涨点效果显著！ xLSTM-UNet是一种结合了LSTM和UNet的混合网络模型，这类模型保留了UNet出色的空间特征提取能力，增加了对时序信息的捕捉和处理能力，能显著提高分割的精度和准确性。 因此，它被广泛应用于医学图像分割、遥感图像处理、工业检测等领域，同时也为复杂场景下的图像处理任务提供了新的思路和创新方向。 我这次整理了8篇LSTM结合UNet的最新paper，给苦于创新点的同学作参考，希望各位看完后可以有些启发，更快写出论文中稿。 论文原文+开源代码需要的同学关注“学姐带你玩AI”公号（不懂的看我主页签名），那边回复“LSTM分割”获取。
  • nnU-Net : The no-new-UNet for automatic segmentation
    nnU-Net is an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation and catalyzing scientific progress as a framework for automated method
  • nnU-Net: a self-configuring method for deep learning-based . . . - Nature
    nnU-Net is a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task in the
  • Segmenting Medical Images: From UNet to Res-UNet and nnUNet - arXiv. org
    nnUNet—evaluating their performance in brain tumour, polyp, and multi-class heart segmentation tasks The analysis focuses on precision, accuracy, recall, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) to assess their clinical applicability In brain tumour segmentation, Res-UNet and nnUNet signifi-





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