Richmond, David

1. Deep Learning based detection of Acute Aortic Syndrome in contrast CT\n images
   2020/04/03 by Manikanta Srikar Yellapragada, Yiting Xie, Yellapragada, Manikanta Srikar +9 · 1 citation
   Computer Science · Engineering · Medicine · #Acute Ischemic Stroke Management #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Imaging and Analysis #electronic engineering #information engineering
2. Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images
   2024/06/08 by Heming Yao, Yao, Heming, Phil Hanslovsky +7 · 2 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Image Processing Techniques and Applications
3. Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction
   2024/12/18 by S. Maleki, Jan-Christian Huetter, Maleki, Sepideh +8 · 2 citations
   Biochemistry, Genetics and Molecular Biology · Medicine · #Advanced Electron Microscopy Techniques and Applications #Advanced Fluorescence Microscopy Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Quantitative Methods (q-bio.QM)
4. Contextualizing biological perturbation experiments through language
   2025/02/28 by Wu, Menghua, Littman, Russell, Levine, Jacob +4 · 3 citations
   #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)