AIMC Topic: Staining and Labeling

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Learning deep features for dead and living breast cancer cell classification without staining.

Scientific reports
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.

Scientific reports
Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to ...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...

A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeru...

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Nature communications
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide trea...

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.

Nature communications
For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied...

Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population.

Journal of bioscience and bioengineering
Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. ...

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Scientific reports
Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we presen...