452 citations · 957 across the 37 of their papers we have counts for
8 papers · 1 filter
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Cheng Peng, Andriy Myronenko, Ali Hatamizadeh +6
Semantic segmentation of 3D medical images is a challenging task due to the high variability of the shape and pattern of objects (such as organs or tumors). Given the recent succes…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
Dong Yang, Andriy Myronenko, Xiaosong Wang +3
Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this tas…
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Andriy Myronenko, Ziyue Xu, Dong Yang +2
Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI). Histology WSIs can have billions of pixels, which create enormous computational…
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs
Md Mahfuzur Rahman Siddiquee, Andriy Myronenko
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation…
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Holger R. Roth, Dong Yang, Wenqi Li +5
Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privac…
The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation
Vishwesh Nath, Dong Yang, Ali Hatamizadeh +4
Deep learning models for medical image segmentation are primarily data-driven. Models trained with more data lead to improved performance and generalizability. However, training is…
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Yingda Xia, Dong Yang, Wenqi Li +15
Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standar…