AIMC Topic: Pathology, Clinical

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Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting...

Deep learning-based molecular morphometrics for kidney biopsies.

JCI insight
Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific...

Artificial intelligence and computational pathology.

Laboratory investigation; a journal of technical methods and pathology
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioin...

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies.

JAMA network open
IMPORTANCE: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.

Rocky road to digital diagnostics: implementation issues and exhilarating experiences.

Journal of clinical pathology
Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in ...

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

The Journal of pathology
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to s...

Deep computational pathology in breast cancer.

Seminars in cancer biology
Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular ...

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective.

Journal of clinical pathology
The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists ar...

Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.

Methods (San Diego, Calif.)
Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodolog...