452 citations · 1.4k across the 37 of their papers we have counts for
35 papers · 1 filter
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Holger R. Roth, Ali Hatamizadeh, Ziyue Xu +4
Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be…
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning
Vishwesh Nath, Dong Yang, Holger R. Roth +1
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning. One of the promising methods to approach this question is active lear…
GradViT: Gradient Inversion of Vision Transformers
Ali Hatamizadeh, Hongxu Yin, Holger Roth +4
In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given…
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Chen Shen, Pochuan Wang, Holger R. Roth +9
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their…
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Yufan He, Dong Yang, Holger Roth +2
Recently, neural architecture search (NAS) has been applied to automatically search high-performance networks for medical image segmentation. The NAS search space usually contains…
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Vishwesh Nath, Dong Yang, Bennett A. Landman +2
Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points that would be beneficial t…