Search Results for author:
Found 6 papers, 5 papers with code
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited.
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e. g. $1024 \times 1024$) directly in pixel-space.
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining
Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue.
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
This allows us to accurately retrieve images even from large-scale databases like LAION-5B.
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.
