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Found 6 papers, 6 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.
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality.
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.
