14 citations · 19 across the 6 of their papers we have counts for
6 papers
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…
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam +4
Localization and characterization of diseases like pneumonia are primary steps in a clinical pipeline, facilitating detailed clinical diagnosis and subsequent treatment planning. A…
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…
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…
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI
Hoo-Chang Shin, Alvin Ihsani, Ziyue Xu +5
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and…