8 citations · 9 across the 7 of their papers we have counts for
7 papers
Learning complete and explainable visual representations from itemized text supervision
Yiwei Lyu, Chenhui Zhao, Soumyanil Banerjee +5
Training vision models with language supervision enables general and transferable representations. However, many visual domains, especially non-object-centric domains such as medic…
Health system learning achieves generalist neuroimaging models
Akhil Kondepudi, Akshay Rao, Chenhui Zhao +14
Frontier artificial intelligence (AI) models, such as OpenAI's GPT-5 and Meta's DINOv3, have advanced rapidly through training on internet-scale public data, yet such systems lack…
Learning neuroimaging models from health system-scale data
Yiwei Lyu, Samir Harake, Asadur Chowdury +18
Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing sig…
Towards Scalable Language-Image Pre-training for 3D Medical Imaging
Chenhui Zhao, Yiwei Lyu, Asadur Chowdury +6
The scalability of current language-image pre-training for 3D medical imaging, such as CT and MRI, is constrained by the need for radiologists to manually curate raw clinical studi…
Super-resolution of biomedical volumes with 2D supervision
Cheng Jiang, Alexander Gedeon, Yiwei Lyu +7
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human…
Step-Calibrated Diffusion for Biomedical Optical Image Restoration
Yiwei Lyu, Sung Jik Cha, Cheng Jiang +7
High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues…
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Jinsu Yoo, Taehoon Kim, Sihaeng Lee +3
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential short…