Mission
The research mission of the Pulmonary Imaging and Computer Vision (PICV) Lab is to develop innovative artificial intelligence (AI) techniques for multimodal medical image processing to characterize physiological structure-function relationships. Specifically we specialize in studying pulmonary diseases using computed tomography (CT), dual-energy CT (DECT), photon-counting CT, and hyper-polarized gas magnetic resonance imaging (HG-MRI).
News
![2024_roh_poster_wrik2](/sites/gerard.lab.uiowa.edu/files/styles/square__1024_x_1024/public/2024-04/wrik1.jpg?h=cac687d8&itok=FGRdlR-1)
2024 College of Engineering Research Open House
Thursday, April 25, 2024
Fantastic work by all the students that presented at the 2024 College of Engineering Research Open House.
![Faizyab_TMI_Overview](/sites/gerard.lab.uiowa.edu/files/styles/square__1024_x_1024/public/2024-02/Faizyab_TMI_Overview_R2.png?h=abba0562&itok=IsuiWRNN)
Paper accepted to TMI
Wednesday, February 21, 2024
Faizyab's manuscript "LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation" was accepted for publication in IEEE Transactions on Medical Imaging. This work proposes LungViT – a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities.
![miccai_mlmi_2023](/sites/gerard.lab.uiowa.edu/files/styles/square__1024_x_1024/public/2023-08/miccai_mlmi_2023.png?h=fc2b0d37&itok=4pc2m3y-)
Paper accepted to MICCAI MLMI workshop
Sunday, August 20, 2023
Faizyab's paper "Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-Supervised Mediastinal Segmentation in CT" was accepted to the MICCAI MLMI workshop. This work uses semi-supervised learning with knowledge distillation to perform mediastinal segmentation in non-contrast CT images.