AI for microscopy and computational pathology
AI to help microscopy image analysis
Tingying Peng’s Helmholtz AI young investigator group’s goal to create new AI methods to help life scientists and pathologists to analyze microscopic images more quantitatively and efficiently, allowing them to extract more knowledge. We would like to develop deep learning methods to address several unique challenges for biomedical imaging, including:
- Domain differences between medical images and natural images where most of deep learning techniques originate;
- Scarcity of high-quality annotated data for efficient network training; and
- The need for explainable algorithms, which usually conflicts with the ‘black-box’ nature of deep neural networks.
The key research strategy we proposed is interpretable deep learning, which blends domain knowledge such as conventional model-based methods and deep learning-based algorithms in a ‘maximise a posterior’ (MAP) manner. This combined approach will leverage the advantages of both model types allowing us to make more accurate predictions while also shedding light on the underlying mechanisms of neural networks for making those predictions.
- Artificial Intelligence for Microscopy: We focus on developing novel AI-based algorithms for microscopy image processing, including cell segmentation, detection, classification and quantification. Particularly, we target a few key challenges in microscopy images:
- Real biological signal mingled with experiment noise and batch variations.
- Inadequate or no ground-truth labels available.
- Datasets of highly unbalanced classes.
We work on both classic microscopy modalities, such as bright-field and fluorescence microscopy, and advanced ones, such as Cryo-electron tomography (Cryo-ET, in collaboration with Dr. Ben Engel’s group at Helmholtz Pioneer Campus) and extended depth-of-field (EDOF) microscope with “Electrically Tunable Lenses” (ETL, in collaboration with Dr. Jan Taucher in GEOMAR)
- Computational Pathology: Our goal is to develop novel AI methods to aid trained professional pathologists in their decision-making, with a special emphasis on three aspects:
- Self-supervised hierarchical image feature extraction that highlights a small number of key pixels leading to accurate diagnoses.
- Confident measures that assess the reliability of AI predictions.
- Human-in-the-loop processes that make AI models more accessible and explainable by facilitating interaction between algorithms and experts.
We collaborate closely with PD. Dr. med Melanie Boxberg in TUM pathology on computational pathology.
T. Peng, L. Lamm, M. Loeffler, N. Ahmed, N. Navab, T. Schroeder, C. Marr, “Background and illumination correction for time-lapse microscopy data with correlated foreground”, International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Lima, Peru, 2020.
T. Peng, M. Boxberg, W. Weichert, N. Navab, and C. Marr, “Multitask learning of a deep k-nearest neighbour network for histopathological image classification and retrieval,” International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Shenzhen, China, 2019
A. Sadafi, N. Koehler, A. Makhro, A. Bogdanova, N. Navab, C. Marr, and T. Peng, “Multiclass Deep Active Learning for Detecting Red Blood Cell Subtypes in Brightfield Microscopy,” International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Shenzhen, China, 2019.
T. Peng, K. Thorn, T. Schroeder, L. Wang, F. J. Theis, C. Marr, and N. Navab, “A BaSiC tool for background and shading correction of optical microscopy images,” Nat. Commun., vol. 8, p. 14836, Jun. 2017. [Paper] [code]
A. Vahadane, T. Peng (equal contribution), A. Sethi, S. Albarqouni, L. Wang, M. Baust, K. Steiger, A. M.Schlitter, I. Esposito, and N. Navab, “Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images,” IEEE Trans. Med. Imaging, Apr. 2016 [Paper][code]
- Lorenz Lamm, PhD student shared with Dr Ben Engel of HPC
- Gindra Rushin, PhD student
- Sophia Wagner, PhD student
- Aidin Biibosunov, Master student & HiWi
- Ye Liung, Master student
- KangKang Wang, Master student
- Manuel Tran, HiWi
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