Characterization of collagen image biomarkers in tissue microenvironment via deep learning approaches
Machine learning
Instrumentation
Biology/Pathology
Weak label and multi-instance learning
Uncertainty and confidence
Discover new image biomarkers in diseases
Interpretability
Representation learning
Explanation models
Computational imaging for pathology
Characterize the relationship between ECM and cells.
Correlation between image biomarkers and non-image vectors
3D pathology, extend the view of ECM, CurveAlign in 3D histopathological images
Cross-modality image synthesis for imaging collagen in histopathological slides
Single image super-resolution for whole slide imaging
Distinguishing pancreatitis and pancreatic cancer using deep weakly supervised learning
Learn with weak labels
Distributed representations
Representation learning and transfer learning
Characterizing Syndecan-1 regulated collagen image biomarkers of breast cancer in histopathological images
Explanation models
Use labels from genetic analysis
Watch dog for deep classifiers
Separate features learned for cells and ECMs
Deep multi-instance learning, weak pathological labels
Extend local classifiers to bigger scales for whole slide images
Extendable open-source software for hardware controlling
Polarization microscopy
Smart microscopy
Visualization of collagen fibers using computational optics
Run-time analysis
Hardware acceleration
Image-to image translation networks
Generative adversarial networks
Curriculum learning
Self-supervised color normalization
Alignment analysis of collagen fibers in volumetric images