AIMC Topic: Pathology, Clinical

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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...

A deep metric learning approach for histopathological image retrieval.

Methods (San Diego, Calif.)
To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of time to seek guidance from confirmed similar images or cases, which is inefficient. Therefore, several histopathological image retrieval methods have b...

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated ...

Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results.

Annals of diagnostic pathology
Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases wer...

Automated Detection and Grading of Non-Muscle-Invasive Urothelial Cell Carcinoma of the Bladder.

The American journal of pathology
Accurate grading of non-muscle-invasive urothelial cell carcinoma is of major importance; however, high interobserver variability exists. A fully automated detection and grading network based on deep learning is proposed to enhance reproducibility. A...

Unsupervised Machine Learning in Pathology: The Next Frontier.

Surgical pathology clinics
Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised mach...

Artificial intelligence as the next step towards precision pathology.

Journal of internal medicine
Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to i...