Peng Lab
Computational image analysis
About
Peng lab develops novel methods to help life scientists and pathologists to analyze microscopic images more quantitatively and efficiently, allowing them to extract more knowledge.
- We focus on developing novel AI-based algorithms for microscopy image processing, including cell segmentation, detection, classification and quantification
- Our work research in classic microscopy modalities, such as bright-field and fluorescence microscopy, and advanced ones, such as Cryo-electron and extended depth-of-field (EDOF) microscope with “Electrically Tunable Lenses”
Publications
Petzold, A. ; Wessely, A. ; Schliep, S. ; Jiang, H. ; Tran, M. ; Koch, E.A. ; Peng, T. ; Starz, H. ; Berking, C. ; Marr, C. ; Heppt, M.V.
Weakly supervised deep learning for cutaneous squamous and basal cell carcinoma in whole-slide histopathology.Jovanovic, A. ; Bright, F.K. ; Sadeghi, A. ; Wicki, B. ; Caño Muñiz, S.E. ; Giannini, G.C. ; Toprak, S. ; Sauteur, L. ; Rodoni, A. ; Wüst, A. ; Lupien, A. ; Borrell, S. ; Grogono, D.M. ; Wheeler, N.E. ; Dehio, P. ; Nemeth, J. ; Pargger, H. ; Thomson, R. ; Bell, S.C. ; Gagneux, S. ; Bryant, J.M. ; Peng, T. ; Diacon, A.H. ; Floto, R.A. ; Abanto, M. ; Boeck, L.
Large-scale testing of antimicrobial lethality at single-cell resolution predicts mycobacterial infection outcomes.Yu, Z. ; Zhang, S. ; Qiao, N. ; Zhao, Y. ; Yu, L. ; Peng, T. ; Zhang, X.Y.
FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation.Contact
