Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task

By Zeyu Jiang, Changxing Ding, Minfeng Liu, Dacheng Tao. BrainLes@MICCAI, 2019

337 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/6eead90d63cc679263ef608121db075b78e03960

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vishwesh Nath
2. Dong Yang
3. Holger R. Roth
4. Yinhai Wang
5. Ali Hatamizadeh
6. Daguang Xu
7. Yucheng Tang

In Computer Vision and Pattern Recognition

1. Vishwesh Nath
2. Caifeng Shan
3. Zheheng Jiang
4. Zhihua Liu
5. Huiyu Zhou
6. Dong Yang
7. Holger R. Roth
8. Lei Tong

In Neurology

1. Vishwesh Nath
2. Dong Yang
3. Holger R. Roth
4. Ali Hatamizadeh
5. Daguang Xu
6. Yucheng Tang

Citations in the corpus

• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (2022, methodology): “Recently, deep learning-based brain tumor segmentation techniques [31,21,32,20] have achieved state-of-the-art performance in various benchmarks [7,35,2].”
• Deep learning based brain tumor segmentation: a survey (2020, background): “To effectively deal with the imbalance problem, researchers try different solutions, such as network cascade and ensemble [64,67,130], multi-task learning [97, 150], and customized loss functions [120].”