452 citations · 747 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
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…
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Xiaosong Wang, Ziyue Xu, Leo Tam +2
Pre-trained models, e.g., from ImageNet, have proven to be effective in boosting the performance of many downstream applications. It is too demanding to acquire large-scale annotat…
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…
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
Dong Yang, Holger Roth, Ziyue Xu +3
Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the…