Related papers: Label-Free Whole Slide Virtual Multi-Staining Usin…
Digital pathology has advanced significantly over the last decade, with Whole Slide Images (WSIs) encompassing vast amounts of data essential for accurate disease diagnosis. High-resolution WSIs are essential for precise diagnosis but…
Virtual immunohistochemistry (IHC) aims to computationally synthesize molecular staining patterns from routine Hematoxylin and Eosin (H\&E) images, offering a cost-effective and tissue-efficient alternative to traditional physical staining.…
Virtual staining offers a promising method for converting Hematoxylin and Eosin (H&E) images into Immunohistochemical (IHC) images, eliminating the need for costly chemical processes. However, existing methods often struggle to utilize…
The detection of nuclei is one of the most fundamental components of computational pathology. Current state-of-the-art methods are based on deep learning, with the prerequisite that extensive labeled datasets are available. The increasing…
Nonlinear optical methods, such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), are able to perform label free imaging, with chemical bonds specificity. Here, we demonstrate that the use of circularly…
In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on…
Stains are essential in histopathology to visualize specific tissue characteristics, with Haematoxylin and Eosin (H&E) serving as the clinical standard. However, pathologists frequently utilize a variety of special stains for the diagnosis…
Accurate face parsing under extreme viewing angles remains a significant challenge due to limited labeled data in such poses. Manual annotation is costly and often impractical at scale. We propose a novel label refinement pipeline that…
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent…
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type,…
Semantic segmentation under domain shift remains a fundamental challenge in computer vision, particularly when labelled training data is scarce. This challenge is particularly exemplified in histopathology image analysis, where the same…
Digital pathology and microscopy image analysis are widely employed in the segmentation of digitally scanned IHC slides, primarily to identify cancer and pinpoint regions of interest (ROI) indicative of tumor presence. However, current ROI…
High-resolution extreme ultraviolet (XUV) imaging remains limited by conventional approaches that require complex optics such as multilayer mirrors and zone plates. These methods are expensive, suffer from chromatic aberrations and narrow…
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional…
Fluorescence microscopy is a powerful tool for imaging biological samples with molecular specificity. In contrast, phase microscopy provides label-free measurement of the sample's refractive index (RI), which is an intrinsic optical…
We introduce a machine-learning-based approach to enhance the sensitivity of optical-extreme ultraviolet (XUV) transient absorption spectroscopy. A reference spectrum is used as input to a three-layer feed-forward neural network, allowing…
Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology…
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks…
Optical spectroscopy and imaging of microscopic samples have opened up a wide range of applications throughout the physical, chemical, and biological sciences. High chemical specificity may be achieved by directly interrogating the…
Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory…