172 citations · 559 across the 44 of their papers we have counts for
8 papers · 1 filter
BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
Yanwu Xu, Li Sun, Wei Peng +7
This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly…
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
Li Sun, Ke Yu, Kayhan Batmanghelich
Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently inco…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…
Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
Rohit Jena, Sumedha Singla, Kayhan Batmanghelich
Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious…
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Li Sun, Ke Yu, Kayhan Batmanghelich
Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging d…
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
Li Sun, Junxiang Chen, Yanwu Xu +3
Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited me…
Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning
Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich +1
Deep learning-based approaches achieve state-of-the-art performance in the majority of image segmentation benchmarks. However, training of such models requires a sizable amount of…