AIMC Topic: Staining and Labeling
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A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.
Nature communications
Nov 26, 2020
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
Nov 16, 2020
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
Oct 17, 2020
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
Oct 15, 2020
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...
Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.
Virchows Archiv : an international journal of pathology
Sep 26, 2020
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...
Humans
Biomarkers, Tumor
Lymphoma, Large B-Cell, Diffuse
Immunohistochemistry
Phenotype
Predictive Value of Tests
Proof of Concept Study
Reproducibility of Results
Microscopy
Proto-Oncogene Proteins c-myc
Antigens, CD20
Deep Learning
Translocation, Genetic
In Situ Hybridization, Fluorescence
Genetic Predisposition to Disease
Image Interpretation, Computer-Assisted
Staining and Labeling
Machine-assisted interpretation of auramine stains substantially increases through-put and sensitivity of microscopic tuberculosis diagnosis.
Tuberculosis (Edinburgh, Scotland)
Sep 19, 2020
Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum ...
Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.
Scientific reports
Sep 1, 2020
Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep ...
Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation.
Medical image analysis
Jul 18, 2020
Nuclei segmentation is a vital step for pathological cancer research. It is still an open problem due to some difficulties, such as color inconsistency introduced by non-uniform manual operations, blurry tumor nucleus boundaries and overlapping tumor...
Semantic segmentation to identify bladder layers from H&E Images.
Diagnostic pathology
Jul 16, 2020
BACKGROUND: Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which recognizes urothelium, lamina propria, muscularis propria, and muscularis...
Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 21, 2020
PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined.
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