Sep 27, 2018 · In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge.

Sep 27, 2018 · The present paper introduces the nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets.

Jan 26, 2019 · In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge.

Aug 10, 2020 · 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 ...

The nnU-Net allows training two types of networks: 2D U-Net and 3D U-Net to perform semantic segmentation of 3D images, with high accuracy and performance.

Aug 4, 2024 · nnU-Net (no-new-Net) stands as a self-configuring framework designed to streamline the development of deep learning models for volumetric (3D) and spatial (2D) ...

Dec 7, 2020 · nnU-Net is a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, ...

The nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets, is introduced and evaluated in ...

Duration: 2:26
Posted: Oct 1, 2020

Aug 9, 2021 · nnU-Net = 'no new net'. In constrast to most other segmentation papers we do not use fancy architectural modifications.