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
Inf. Fusion 127:103840 (2026)
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.
In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). 2026. 318-327 (Lect. Notes Comput. Sc. ; 15972 LNCS)
Chobola, T. ; Schnabel, J.A. ; Peng, T.
Lightweight Data-Free Denoising for Detail-Preserving Biomedical Image Restoration.
Nat. Med., DOI: 10.1038/s41591-025-03982-3 (2025)
Ding, T. ; Wagner, S. ; Song, A.H. ; Chen, R.J. ; Lu, M.Y. ; Zhang, A. ; Vaidya, A.J. ; Jaume, G. ; Shaban, M. ; Kim, A. ; Williamson, D.F.K. ; Robertson, H. ; Chen, B. ; Almagro-Pérez, C. ; Doucet, P. ; Sahai, S. ; Chen, C. ; Chen, C.S. ; Komura, D. ; Kawabe, A. ; Ochi, M. ; Sato, S. ; Yokose, T. ; Miyagi, Y. ; Ishikawa, S. ; Gerber, G. ; Peng, T. ; Le, L.P. ; Mahmood, F.
A multimodal whole-slide foundation model for pathology.Contact
