AIMC Topic: Pathology, Clinical
Clear Filters Showing 41 to 50 of 78 articles
Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis.
Diagnostic and interventional imaging
Oct 16, 2019
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.
Scientific reports
Oct 4, 2019
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...
Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach.
The American journal of pathology
Sep 18, 2019
The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentially important role in patient treatment and prognosis. Despite this attention, false-positive predic...
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.
Medical image analysis
Aug 21, 2019
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with...
Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.
Nature reviews. Clinical oncology
Aug 9, 2019
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks m...
Machine learning for tissue diagnostics in oncology: brave new world.
British journal of cancer
Aug 9, 2019
Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified appro...
Machine learning approaches for pathologic diagnosis.
Virchows Archiv : an international journal of pathology
Jun 20, 2019
Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognit...
Pathology Image Analysis Using Segmentation Deep Learning Algorithms.
The American journal of pathology
Jun 11, 2019
With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and...
Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.
Journal of the American College of Radiology : JACR
Jun 4, 2019
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-mode...
Impact of pre-analytical variables on deep learning accuracy in histopathology.
Histopathology
May 16, 2019
AIMS: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many ...
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